Declarative Machine Learning Systems: Big Tech Level ML Without a Big Tech Team // Piero Molino // MLOps Coffee Sessions #101

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محتوای ارائه شده توسط Demetrios. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Demetrios یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

<div class="span index">1</div> <span><a class="" data-remote="true" data-type="html" href="/series/accidental-ceo-podcast">Accidental CEO Podcast</a></span>

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Accidental CEO Podcast

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۳ روز پیش۳d ago

لغو اشتراک

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Dive into the heart of creativity and business with the Accidental CEO Podcast, your go-to audio hangout for turning passion into a thriving business. Hosted by Nata Salvatori, a seasoned wedding photographer and savvy business educator, this podcast peels back the curtain on the accidental journey from creative professional to CEO. Whether you stumbled upon the role of entrepreneur or have always dreamt of leading your own venture, this podcast is crafted for you. Each episode is a blend of inspiring stories, practical strategies, and real-world advice to navigate the complexities of running a creative business. From building a brand that resonates with your ideal clients to mastering the art of sustainable growth, Nata and a roster of guest experts offer insights into making the leap from creative to CEO not just possible, but profitable and fulfilling. The Accidental CEO Podcast is more than just a podcast; it's a community of creative minds eager to transform their accidental titles into intentional successes. So, if you're passionate about what you do but unsure how to fall in love with the business side of things, let Nata guide you. Subscribe to the Accidental CEO Podcast and join a vibrant community of creative entrepreneurs ready to make their mark on the business world. Together, let's turn your passion project into your success story. Connect with Nata: accidentalceopod.com instagram.com/accidentalceopod

۳ سال پیش 58:46

M4A•خانه قسمت

MLOps Coffee Sessions #101 with Piero Molino, Declarative Machine Learning Systems: Big Tech Level ML Without a Big Tech Team co-hosted by Vishnu Rachakonda.
// Abstract
Declarative Machine Learning Systems are the next step in the evolution of Machine Learning infrastructure.

With such systems, organizations can marry the flexibility of low-level APIs with the simplicity of AutoML.
Companies adopting such systems can increase the speed of machine learning development, reaching the quality and scalability that only big tech companies could achieve until now, without the need for a team of several thousand people.
Predibase is the turnkey solution for adopting declarative ML systems at an enterprise scale.
// Bio
Piero Molino is CEO and co-founder of Predibase, a company redefining ML tooling. Most recently, he has been Staff Research Scientist at Stanford University working on Machine Learning systems and algorithms in Prof. Chris Ré's' Hazy group. Piero completed a Ph.D. in Question Answering at the University of Bari, Italy. Founded QuestionCube, a startup that built a framework for semantic search and QA. Worked for Yahoo Labs in Barcelona on learning to rank, IBM Watson in New York on natural language processing with deep learning, and then joined Geometric Intelligence, where he worked on grounded language understanding.
After Uber acquired Geometric Intelligence, Piero became one of the founding members of Uber AI Labs. At Uber, he worked on research topics including Dialogue Systems, Language Generation, Graph Representation Learning, Computer Vision, Reinforcement Learning, and Meta-Learning. He also worked on several deployed systems like COTA, an ML and NLP model for Customer Support, Dialogue Systems for driver's hands-free dispatch, the Uber Eats Recommender System with graph learning and collusion detection. He is the author of Ludwig, a Linux-Foundation-backed open source declarative deep learning framework.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs

MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
Website: http://w4nderlu.st
http://ludwig.ai https://medium.com/ludwig-ai
Declarative Machine Learning Systems paper By Piero Molino, Christopher Ré: https://cacm.acm.org/magazines/2022/1/257445-declarative-machine-learning-systems/fulltext
Slip of the Keyboard by Sir Terry Pratchett: https://www.terrypratchettbooks.com/books/a-slip-of-the-keyboard/
The Listening Society book series by Hanzi Freinacht: https://www.amazon.com/Listening-Society-Metamodern-Politics-Guides-ebook/dp/B074MKQ4LR
--------------- ✌️Connect With Us ✌️ -------------
Join our slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Piero on LinkedIn: https://www.linkedin.com/in/pieromolino/?locale=en_US

452 قسمت

Declarative Machine Learning Systems: Big Tech Level ML Without a Big Tech Team // Piero Molino // MLOps Coffee Sessions #101

MLOps.community

56 subscribers

published ۳ سال پیش

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M4A•خانه قسمت

452 قسمت

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From the Legal Trenches to Tech // Nick Coleman // #332 35:51

۳ روز پیش35:51

35:51

From the Legal Trenches to Tech // MLOps Podcast #332 with Nick Coleman, Attorney/Founder of LexMed. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Nick Coleman shares his journey from high-volume Social Security disability practice to founding LexMed, a legal tech startup leveraging AI to transform how attorneys handle complex cases. He'll discuss LexMed's dual AI platforms: Hearing Echo, which automates transcription and analysis of disability hearings with speaker identification and critical testimony validation, and ChartVision, which combines human medical abstraction with AI to extract and map medical evidence to disability criteria. Nick will explain how "vibe coding" has dramatically reduced friction between his subject matter expertise and technical implementation, enabling rapid prototyping that preserves legal insights through development. By bridging domain knowledge and technology, LexMed has created solutions that address the real-world challenges he experienced firsthand in his high-volume disability practice, offering valuable lessons for AI implementation in other specialized fields. // Bio Nick Coleman is the founder and CEO of LexMed, a legal tech startup applying advanced AI to transform the practice of law. As a Social Security disability attorney with extensive appellate experience, Nick identified critical inefficiencies in legal workflows that technology could solve. LexMed's flagship product, Hearing Echo, leverages speech recognition and natural language processing to automate the transcription and analysis of disability hearing audio, dramatically improving case management for attorneys. Nick holds an AV Preeminent rating from Martindale-Hubbell, has been recognized as a Super Lawyers Rising Star, and serves on the Arkansas Bar Artificial Intelligence Task Force. With deep expertise at the intersection of law and technology, Nick is passionate about democratizing access to justice through innovative AI solutions. // Related Links Website: www.lexmed.ai ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [https://go.mlops.community/slack] Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register] MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Nick on LinkedIn: /nicklcoleman/…

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The Rise of Sovereign AI and Global AI Innovation in a World of US Protectionism // Frank Meehan // MLOps Podcast #331 54:13

۷ روز پیش54:13

54:13

The Rise of Sovereign AI and Global AI Innovation in a World of US Protectionism // MLOps Podcast #331 with Frank Meehan, Founder and CEO of Frontier One AI. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract “The awakening of every single country is that they have to control their AI intelligence and not outsource their data" - Jensen Huang. Sovereign AI is rapidly becoming a fundamental national utility, much like defense, energy or telecoms. Nations worldwide recognize that AI sovereignty—having control over your AI infrastructure, data, and models—is essential for economic progress, security, and especially independence - especially when the US is pushing protectionism and trying to prevent global AI innovation. Of course this has the opposite effect - DeepSeek created by a Hedge Fund in China; India building the world's largest AI data centre (3 GW), and global software teams scaling, learning and building faster than ever before. However most countries lack the talent, financing and experience to implement Sovereign AI for their requirements - and it is our belief at Frontier One, that one of the biggest markets for AI applications, cloud services and GPUs will be global governments. We see it already - with $10B of GPUs in 2024 bought directly by governments - and it's rapidly expanding. We will talk about what Sovereign AI is - both infrastructure and software details / why it is crucial for a nation / how to get involved as part of the MLOps community. // Bio Co-Founder of Frontier One - building Sovereign AI Factories and Cloud software for global markets. Frank is a 2X CEO | 2X CMO (with 2X exits + 1 IPO NYSE), Board Director (Spotify, Siri) and Investor (SparkLabs Group) with 20+ years of experience in creating and growing leading brands, products and companies. Chair of Improvability, automating due diligence and reporting for corporates, foundations and Governments with AI. Co-founder and partner at SparkLabs Group - investors in OpenAI, Anthropic, 88 Rising, Discord, Animoca, Andela, Vectara, Kneron, Messari, Lifesum + 400 companies in our portfolio. Investment Committee and LP at SparkLabs Cultiv8 with 56 investments in consumer food and regenerative agriculture companies. Co-founder and CMO - later CEO - of Equilibrium AI (Singapore), building it to one of the leading ESG and Carbon data management platforms globally. Equilibrium was acquired by FiscalNote in 2021, where he joined the senior leadership team, running the ESG business globally, and helping the company IPO in 2022 on the NYSE at $1.1B valuation. Board director at Spotify (2009-2012); Siri (2009-2010 exited to Apple); Lifesum (leading AI health app with 50 million users), seed investor in 88 Rising (Asia’s leading independent music label); CEO/CMO and co-founder at INQ Mobile (mobile internet pioneer); and Global Director for devices and products at 3 Mobile. Started as a software developer with Ericsson Mobile in Sweden, after graduating from KTH in Stockholm and the University of Sydney with a Bachelor of Mechanical Engineering, and Master of Science in Fluid Mechanics. // Related Links https://www.frontierone.ai/ and https://www.sparklabsgroup.com ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [https://go.mlops.community/slack] Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register] MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Frank on LinkedIn: /frankmeehan/…

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A New Way of Building with AI 1:04:49

۹ روز پیش1:04:49

1:04:49

Thanks to MLflow for supporting this episode — the platform helping teams track, manage, and deploy ML and GenAI projects with ease. Try it free at mlflow.org . What if AI could build and maintain your software—like a co-worker who never forgets state? In this episode, Jiquan Ngiam chats with Demetrios about agents that actually do the work: parsing emails, updating spreadsheets, and reshaping how we design software itself. Less hype, more hands-on AI—tune in for a glimpse at the future of truly personalized computing. // Bio Jiquan Ngiam is the Co-Founder and CEO of Lutra AI, with deep expertise in artificial intelligence and machine learning. He was previously at Google Brain, Coursera, and in the Stanford CS Ph.D. program advised by Andrew Ng. He helped develop the first online courses in Machine Learning, and is now building agentic AI systems that can complete tasks for us. // Related Links https://www.youtube.com/@LutraAI #api #llm #lutra #costefficiency #latentspace ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Jiquan on LinkedIn: /jngiam/ Timestamps: [00:00] Agents That Actually Do Work [08:21] Building Tables With AI Help [12:54] Guardrails for Smarter Code [16:35 - 18:00] MLFlow Ad[18:30] What’s Next for MCP? [23:23] AI as Your Data Conductor [31:13] Rethinking AI + Data Stacks [32:10] Sandbox Security, Real Risks [40:48] Smarter Reviews, Powered by Use [46:08] Cost vs. Quality in AI [52:00] Podcast Editing Gets Creative [56:27] Transparent UIs, Powered by AI [01:00:28] Can AI Learn Good Taste? [01:04:45] Peeking Into Wild AI Futures…

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Inside Uber’s AI Revolution - Everything about how they use AI/ML 45:23

14 روز پیش45:23

45:23

Kai Wang joins the MLOps Community podcast LIVE to share how Uber built and scaled its ML platform, Michelangelo. From mission-critical models to tools for both beginners and experts, he walks us through Uber’s AI playbook—and teases plans to open-source parts of it. // Bio Kai Wang is the product lead of the AI platform team at Uber, overseeing Uber's internal end-to-end ML platform called Michelangelo that powers 100% Uber's business-critical ML use cases. // Related Links Uber GenAI: https://www.uber.com/blog/from-predictive-to-generative-ai/ #uber #podcast #ai #machinelearning ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Kai on LinkedIn: /kai-wang-67457318/ Timestamps: [00:00] Rethinking AI Beyond ChatGPT [04:01] How Devs Pick Their Tools [08:25] Measuring Dev Speed Smartly [10:14] Predictive Models at Uber [13:11] When ML Strategy Shifts [15:56] Smarter Uber Eats with AI [19:29] Summarizing Feedback with ML [23:27] GenAI That Users Notice [27:19] Inference at Scale: Michelangelo [32:26] Building Uber’s AI Studio [33:50] Faster AI Agents, Less Pain [39:21] Evaluating Models at Uber [42:22] Why Uber Open-Sourced Machanjo [44:32] What Fuels Uber’s AI Team…

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The Missing Data Stack for Physical AI 52:42

17 روز پیش52:42

52:42

The Missing Data Stack for Physical AI // MLOps Podcast #328 with Nikolaus West, CEO of Rerun. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Nikolaus West, CEO of Rerun, breaks down the challenges and opportunities of physical AI—AI that interacts with the real world. He explains why traditional software falls short in dynamic environments and how visualization, adaptability, and better tooling are key to making robotics and spatial computing more practical. // Bio Niko is a second-time founder and software engineer with a computer vision background from Stanford. He’s a fanatic about bringing great computer vision and robotics products to the physical world. // Related Links Website: rerun.io ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [ https://go.mlops.community/slack ] Follow us on X/Twitter [ @mlopscommunity ]( https://x.com/mlopscommunity ) or [LinkedIn]( https://go.mlops.community/linkedin )] Sign up for the next meetup: [ https://go.mlops.community/register ] MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Niko on LinkedIn: /NikolausWest Timestamps: [00:00] Niko's preferred coffee [00:35] Physical AI vs Robotics Debate [04:40] IoT Hype vs Reality [12:16] Physical AI Lifecycle Overview [20:05] AI Constraints in Robotics [23:42] Data Challenges in Robotics [33:37] Open Sourcing AI Tools [39:36] Rerun Platform Integration [40:57] Data Integration for Insights [45:02] Data Pipelines and Quality [49:19] Robotics Design Trade-offs [52:25] Wrap up…

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AI Reliability, Spark, Observability, SLAs and Starting an AI Infra Company 1:37:22

22 روز پیش1:37:22

1:37:22

LLMs are reshaping the future of data and AI—and ignoring them might just be career malpractice. Yoni Michael and Kostas Pardalis unpack what’s breaking, what’s emerging, and why inference is becoming the new heartbeat of the data pipeline. // Bio Kostas Pardalis Kostas is an engineer-turned-entrepreneur with a passion for building products and companies in the data space. He’s currently the co-founder of Typedef. Before that, he worked closely with the creators of Trino at Starburst Data on some exciting projects. Earlier in his career, he was part of the leadership team at Rudderstack, helping the company grow from zero to a successful Series B in under two years. He also founded Blendo in 2014, one of the first cloud-based ELT solutions. Yoni Michael Yoni is the Co-Founder of typedef, a serverless data platform purpose-built to help teams process unstructured text and run LLM inference pipelines at scale. With a deep background in data infrastructure, Yoni has spent over a decade building systems at the intersection of data and AI — including leading infrastructure at Tecton and engineering teams at Salesforce. Yoni is passionate about rethinking how teams extract insight from massive troves of text, transcripts, and documents — and believes the future of analytics depends on bridging traditional data pipelines with modern AI workflows. At Typedef, he’s working to make that future accessible to every team, without the complexity of managing infrastructure. // Related Links Website: https://www.typedef.ai https://techontherocks.show https://www.cpard.xyz ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Kostas on LinkedIn: /kostaspardalis/ Connect with Yoni on LinkedIn: / yonimichael / Timestamps: [00:00] Breaking Tools, Evolving Data Workloads [06:35] Building Truly Great Data Teams [10:49] Making Data Platforms Actually Useful [18:54] Scaling AI with Native Integration [24:04] Empowering Employees to Build Agents [28:17] Rise of the AI Sherpa [36:09] Real AI Infrastructure Pain Points [38:05] Fixing Gaps Between Data, AI [46:04] Smarter Decisions Through Better Data [50:18] LLMs as Human-Machine Interfaces [53:40] Why Summarization Still Falls Short [01:01:15] Smarter Chunking, Fixing Text Issues [01:09:08] Evaluating AI with Canary Pipelines [01:11:46] Finding Use Cases That Matter [01:17:38] Cutting Costs, Keeping AI Quality [01:25:15] Aligning MLOps to Business Outcomes [01:29:44] Communities Thrive on Cross-Pollination [01:34:56] Evaluation Tools Quietly Consolidating…

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Greg Kamradt: Benchmarking Intelligence | ARC Prize 48:30

25 روز پیش48:30

48:30

What makes a good AI benchmark? Greg Kamradt joins Demetrios to break it down—from human-easy, AI-hard puzzles to wild new games that test how fast models can truly learn. They talk hidden datasets, compute tradeoffs, and why benchmarks might be our best bet for tracking progress toward AGI. It’s nerdy, strategic, and surprisingly philosophical. // Bio Greg has mentored thousands of developers and founders, empowering them to build AI-centric applications.By crafting tutorial-based content, Greg aims to guide everyone from seasoned builders to ambitious indie hackers.Greg partners with companies during their product launches, feature enhancements, and funding rounds. His objective is to cultivate not just awareness, but also a practical understanding of how to optimally utilize a company's tools.He previously led Growth @ Salesforce for Sales & Service Clouds in addition to being early on at Digits, a FinTech Series-C company. // Related Links Website: https://gregkamradt.com/ YouTube channel: https://www.youtube.com/@DataIndependent ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Greg on LinkedIn: /gregkamradt/ Timestamps: [00:00] Human-Easy, AI-Hard [05:25] When the Model Shocks Everyone [06:39] “Let’s Circle Back on That Benchmark…” [09:50] Want Better AI? Pay the Compute Bill [14:10] Can We Define Intelligence by How Fast You Learn? [16:42] Still Waiting on That Algorithmic Breakthrough [20:00] LangChain Was Just the Beginning [24:23] Start With Humans, End With AGI [29:01] What If Reality’s Just... What It Seems? [32:21] AI Needs Fewer Vibes, More Predictions [36:02] Defining Intelligence (No Pressure) [36:41] AI Building AI? Yep, We're Going There [40:13] Open Source vs. Prize Money Drama [43:05] Architecting the ARC Challenge [46:38] Agent 57 and the Atari Gauntlet…

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Bridging the Gap Between AI and Business Data // Deepti Srivastava // #325 57:13

29 روز پیش57:13

57:13

Bridging the Gap Between AI and Business Data // MLOps Podcast #325 with Deepti Srivastava, Founder and CEO at Snow Leopard. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract I’m sure the MLOps community is probably aware – it's tough to make AI work in enterprises for many reasons, from data silos, data privacy and security concerns, to going from POCs to production applications. But one of the biggest challenges facing businesses today, that I particularly care about, is how to unlock the true potential of AI by leveraging a company’s operational business data. At Snow Leopard, we aim to bridge the gap between AI systems and critical business data that is locked away in databases, data warehouses, and other API-based systems, so enterprises can use live business data from any data source – whether it's database, warehouse, or APIs – in real time and on demand, natively. In this interview, I'd like to cover Snow Leopard’s intelligent data retrieval approach that can leverage business data directly and on-demand to make AI work. // Bio Deepti is the founder and CEO of Snow Leopard AI, a platform that helps teams build AI apps using their live business data, on-demand. She has nearly 2 decades of experience in data platforms and infrastructure. As Head of Product at Observable, Deepti led the 0→1 product and GTM strategy in the crowded data analytics market. Before that, Deepti was the founding PM for Google Spanner, growing it to thousands of internal customers (Ads, PlayStore, Gmail, etc.), before launching it externally as a seminal cloud database service. Deepti started her career as a distributed systems engineer in the RAC database kernel at Oracle. // Related Links Website: https://www.snowleopard.ai/ AI SQL Data Analyst // Donné Stevenson - https://youtu.be/hwgoNmyCGhQ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [ https://go.mlops.community/slack ] Follow us on X/Twitter [ @mlopscommunity ]( https://x.com/mlopscommunity ) or [LinkedIn]( https://go.mlops.community/linkedin )] Sign up for the next meetup: [ https://go.mlops.community/register ] MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Deepti on LinkedIn: /thedeepti/ Timestamps: [00:00] Deepti's preferred coffee [00:49] MLflow vs Kubeflow Debate [04:58] GenAI Data Integration Challenges [09:02] GenAI Sidecar Spicy Takes [14:07] Troubleshooting LLM Hallucinations [19:03] AI Overengineering and Hype [25:06] Self-Serve Analytics Governance [33:29] Dashboards vs Data Quality [37:06] Agent Database Context Control [43:00] LLM as Orchestrator [47:34] Tool Call Ownership Clarification [51:45] MCP Server Challenges [56:52] Wrap up…

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The Creator of FastAPI’s Next Chapter // Sebastián Ramírez // #324 1:09:37

۴ weeks پیش1:09:37

1:09:37

The Creator of FastAPI’s Next Chapter // MLOps Podcast #324 with Sebastián Ramírez, Developer at FastAPI Labs. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract The creator of FastAPI is back with a new chapter—FastAPI Cloud. From building one of the most loved dev tools to launching a company, Sebastián Ramírez shares how open source, developer experience, and a dash of humor are shaping the future of APIs. // Bio Sebastián Ramírez (also known as Tiangolo) is the creator of FastAPI, Typer, SQLModel, Asyncer, and several other widely used open-source tools. He has collaborated with companies and teams around the world—from Latin America to the Middle East, Europe, and the United States—building a range of products and custom solutions focused on APIs, data processing, distributed systems, and machine learning. Today, he works full time on FastAPI and its growing ecosystem. // Related Links Website: https://tiangolo.com/ FastAPI: https://fastapi.tiangolo.com/ FastAPI Cloud: https://fastapicloud.com/ FastAPI for Machine Learning // Sebastián Ramírez // MLOps Coffee Sessions #96 - https://youtu.be/NpvRhZnkEFg ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [https://go.mlops.community/slack] Follow us on X/Twitter [ @mlopscommunity ]( https://x.com/mlopscommunity ) or [LinkedIn]( https://go.mlops.community/linkedin )] Sign up for the next meetup: [ https://go.mlops.community/register ] MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Tiangolo on LinkedIn: /tiangolo Timestamps: [00:00] Sebastián's preferred coffee [00:15] Takeaways [01:43] Why Pydantic is Awesome [06:47] ML Background and FastAPI [10:44] NASA FastAPI Emojis [15:21] FastAPI Cloud Journey [26:07] FastAPI Cloud Open-Source Balance [31:45] Basecamp Design Philosophy [35:30] AI Abstraction Strategies [42:56] Engineering vs Developer Experience [51:40] Dogfooding and Docs Strategy [59:44] Code Simplicity and Trust [1:04:26] Scaling Without Losing Vision [1:08:20] FastAPI Cloud Signup [1:09:23] Wrap up…

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Everything Hard About Building AI Agents Today 47:02

۵ weeks پیش47:02

47:02

Willem Pienaar and Shreya Shankar discuss the challenge of evaluating agents in production where "ground truth" is ambiguous and subjective user feedback isn't enough to improve performance. The discussion breaks down the three "gulfs" of human-AI interaction—Specification, Generalization, and Comprehension—and their impact on agent success. Willem and Shreya cover the necessity of moving the human "out of the loop" for feedback, creating faster learning cycles through implicit signals rather than direct, manual review.The conversation details practical evaluation techniques, including analyzing task failures with heat maps and the trade-offs of using simulated environments for testing. Willem and Shreya address the reality of a "performance ceiling" for AI and the importance of categorizing problems your agent can, can learn to, or will likely never be able to solve. // Bio Shreya Shankar PhD student in data management for machine learning. Willem Pienaar Willem Pienaar, CTO of Cleric, is a builder with a focus on LLM agents, MLOps, and open source tooling. He is the creator of Feast, an open source feature store, and contributed to the creation of both the feature store and MLOps categories. Before starting Cleric, Willem led the open source engineering team at Tecton and established the ML platform team at Gojek, where he built high scale ML systems for the Southeast Asian decacorn. // Related Links https://www.google.com/about/careers/applications/?utm_campaign=profilepage&utm_medium=profilepage&utm_source=linkedin&src=Online/LinkedIn/linkedin_page https://cleric.ai/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Shreya on LinkedIn: /shrshnk Connect with Willem on LinkedIn: /willempienaar Timestamps: [00:00] Trust Issues in AI Data [04:49] Cloud Clarity Meets Retrieval [09:37] Why Fast AI Is Hard [11:10] Fixing AI Communication Gaps [14:53] Smarter Feedback for Prompts [19:23] Creativity Through Data Exploration [23:46] Helping Engineers Solve Faster [26:03] The Three Gaps in AI [28:08] Alerts Without the Noise [33:22] Custom vs General AI [34:14] Sharpening Agent Skills [40:01] Catching Repeat Failures [43:38] Rise of Self-Healing Software [44:12] The Chaos of Monitoring AI…

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Tricks to Fine Tuning // Prithviraj Ammanabrolu // #318 54:01

۵ weeks پیش54:01

54:01

Tricks to Fine Tuning // MLOps Podcast #318 with Prithviraj Ammanabrolu, Research Scientist at Databricks . Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Prithviraj Ammanabrolu drops by to break down Tao fine-tuning—a clever way to train models without labeled data. Using reinforcement learning and synthetic data, Tao teaches models to evaluate and improve themselves. Raj explains how this works, where it shines (think small models punching above their weight), and why it could be a game-changer for efficient deployment. // Bio Raj is an Assistant Professor of Computer Science at the University of California, San Diego, leading the PEARLS Lab in the Department of Computer Science and Engineering (CSE). He is also a Research Scientist at Mosaic AI, Databricks, where his team is actively recruiting research scientists and engineers with expertise in reinforcement learning and distributed systems. Previously, he was part of the Mosaic team at the Allen Institute for AI. He earned his PhD in Computer Science from the School of Interactive Computing at Georgia Tech, advised by Professor Mark Riedl in the Entertainment Intelligence Lab. // Related Links Website: https://www.databricks.com/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [ https://go.mlops.community/slack ] Follow us on X/Twitter [ @mlopscommunity ]( https://x.com/mlopscommunity ) or [LinkedIn]( https://go.mlops.community/linkedin )] Sign up for the next meetup: [ https://go.mlops.community/register ] MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Raj on LinkedIn: /rajammanabrolu Timestamps: [00:00] Raj's preferred coffee [00:36] Takeaways [01:02] Tao Naming Decision [04:19] No Labels Machine Learning [08:09] Tao and TAO breakdown [13:20] Reward Model Fine-Tuning [18:15] Training vs Inference Compute [22:32] Retraining and Model Drift [29:06] Prompt Tuning vs Fine-Tuning [34:32] Small Model Optimization Strategies [37:10] Small Model Potential [43:08] Fine-tuning Model Differences [46:02] Mistral Model Freedom [53:46] Wrap up…

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Packaging MLOps Tech Neatly for Engineers and Non-engineers // Jukka Remes // #322 55:30

۵ weeks پیش55:30

55:30

Packaging MLOps Tech Neatly for Engineers and Non-engineers // MLOps Podcast #322 with Jukka Remes, Senior Lecturer (SW dev & AI), AI Architect at Haaga-Helia UAS, Founder & CTO at 8wave AI. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract AI is already complex—adding the need for deep engineering expertise to use MLOps tools only makes it harder, especially for SMEs and research teams with limited resources. Yet, good MLOps is essential for managing experiments, sharing GPU compute, tracking models, and meeting AI regulations. While cloud providers offer MLOps tools, many organizations need flexible, open-source setups that work anywhere—from laptops to supercomputers. Shared setups can boost collaboration, productivity, and compute efficiency.In this session, Jukka introduces an open-source MLOps platform from Silo AI, now packaged for easy deployment across environments. With Git-based workflows and CI/CD automation, users can focus on building models while the platform handles the MLOps.// BioFounder & CTO, 8wave AI | Senior Lecturer, Haaga-Helia University of Applied SciencesJukka Remes has 28+ years of experience in software, machine learning, and infrastructure. Starting with SW dev in the late 1990s and analytics pipelines of fMRI research in early 2000s, he’s worked across deep learning (Nokia Technologies), GPU and cloud infrastructure (IBM), and AI consulting (Silo AI), where he also led MLOps platform development. Now a senior lecturer at Haaga-Helia, Jukka continues evolving that open-source MLOps platform with partners like the University of Helsinki. He leads R&D on GenAI and AI-enabled software, and is the founder of 8wave AI, which develops AI Business Operations software for next-gen AI enablement, including regulatory compliance of AI. // Related Links Open source -based MLOps k8s platform setup originally developed by Jukka's team at Silo AI - free for any use and installable in any environment from laptops to supercomputing: https://github.com/OSS-MLOPS-PLATFORM/oss-mlops-platform Jukka's new company: https://8wave.ai ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [ https://go.mlops.community/slack ] Follow us on X/Twitter [ @mlopscommunity ]( https://x.com/mlopscommunity ) or [LinkedIn]( https://go.mlops.community/linkedin )] Sign up for the next meetup: [ https://go.mlops.community/register ] MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Jukka on LinkedIn: /jukka-remes Timestamps: [00:00] Jukka's preferred coffee [00:39] Open-Source Platform Benefits [01:56] Silo MLOps Platform Explanation [05:18] AI Model Production Processes [10:42] AI Platform Use Cases [16:54] Reproducibility in Research Models [26:51] Pipeline setup automation [33:26] MLOps Adoption Journey [38:31] EU AI Act and Open Source [41:38] MLOps and 8wave AI [45:46] Optimizing Cross-Stakeholder Collaboration [52:15] Open Source ML Platform [55:06] Wrap up…

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Hard Learned Lessons from Over a Decade in AI 48:42

۶ weeks پیش48:42

48:42

Tecton⁠ Founder and CEO Mike Del Balso talks about what ML/AI use cases are core components generating Millions in revenue. Demetrios and Mike go through the maturity curve that predictive Machine Learning use cases have gone through over the past 5 years, and why a feature store is a primary component of an ML stack. // Bio Mike Del Balso is the CEO and co-founder of Tecton, where he’s building the industry’s first feature platform for real-time ML. Before Tecton, Mike co-created the Uber Michelangelo ML platform. He was also a product manager at Google where he managed the core ML systems that power Google’s Search Ads business. He studied Applied Science, Electrical & Computer Engineering at the University of Toronto. // Related Links Website: ⁠www.tecton.ai⁠ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: ⁠https://go.mlops.community/TYExplore⁠ MLOps Swag/Merch: [ ⁠https://shop.mlops.community/⁠ ] Connect with Demetrios on LinkedIn: ⁠/dpbrinkm⁠ Connect with Mike on LinkedIn: ⁠/michaeldelbalso⁠ Timestamps: [00:00] Smarter decisions, less manual work [03:52] Data pipelines: pain and fixes [08:45] Why Tecton was born [11:30] ML use cases shift [14:14] Models for big bets [18:39] Build or buy drama [20:20] Fintech's data playbook [23:52] What really needs real-time [28:07] Speeding up ML delivery [32:09] Valuing ML is tricky [35:29] Simplifying ML toolkits [37:18] AI copilots in action [42:13] AI that fights fraud [45:07] Teaming up across coasts [46:43] Tecton + Generative AI?…

26 weeks پیش47:07

47:07

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Machine Learning, AI Agents, and Autonomy // Egor Kraev // #282 1:05:20

27 weeks پیش1:05:20

1:05:20

Since three years, Egor is bringing the power of AI to bear at Wise , across domains as varied as trading algorithms for Treasury, fraud detection, experiment analysis and causal inference, and recently the numerous applications unlocked by large language models. Open-source projects initiated and guided by Egor include wise-pizza, causaltune, and neural-lifetimes, with more on the way. Machine Learning, AI Agents, and Autonomy // MLOps Podcast #282 with Egor Kraev, Head of AI at Wise Plc. // Abstract Demetrios chats with Egor Kraev, principal AI scientist at Wise, about integrating large language models (LLMs) to enhance ML pipelines and humanize data interactions. Egor discusses his open-source MotleyCrew framework, career journey, and insights into AI's role in fintech, highlighting its potential to streamline operations and transform organizations. // Bio Egor first learned mathematics in the Russian tradition, then continued his studies at ETH Zurich and the University of Maryland. Egor has been doing data science since last century, including economic and human development data analysis for nonprofits in the US, the UK, and Ghana, and 10 years as a quant, solutions architect, and occasional trader at UBS then Deutsche Bank. Following last decade's explosion in AI techniques, Egor became Head of AI at Mosaic Smart Data Ltd, and for the last four years is bringing the power of AI to bear at Wise, in a variety of domains, from fraud detection to trading algorithms and causal inference for A/B testing and marketing. Egor has multiple side projects such as RL for molecular optimization, GenAI for generating and solving high school math problems, and others. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links https://github.com/transferwise/wise-pizza https://github.com/py-why/causaltune https://www.linkedin.com/posts/egorkraev_a-talk-on-experimentation-best-practices-activity-7092158531247755265-q0kt?utm_source=share&utm_medium=member_desktop --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Egor on LinkedIn: https://www.linkedin.com/in/egorkraev/…

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Re-Platforming Your Tech Stack // Michelle Marie Conway & Andrew Baker // #281 51:14

28 weeks پیش51:14

51:14

Re-Platforming Your Tech Stack // MLOps Podcast #281 with Michelle Marie Conway, Lead Data Scientist at Lloyds Banking Group and Andrew Baker, Data Science Delivery Lead at Lloyds Banking Group. // Abstract Lloyds Banking Group is on a mission to embrace the power of cloud and unlock the opportunities that it provides. Andrew, Michelle, and their MLOps team have been on a journey over the last 12 months to take their portfolio of circa 10 Machine Learning models in production and migrate them from an on-prem solution to a cloud-based environment. During the podcast, Michelle and Andrew share their reflections as well as some dos (and don’ts!) of managing the migration of an established portfolio. // Bio Michelle Marie Conway Michelle is a Lead Data Scientist in the high-performance data science team at Lloyds Banking Group. With deep expertise in managing production-level Python code and machine learning models, she has worked alongside fellow senior manager Andrew to drive the bank's transition to the Google Cloud Platform. Together, they have played a pivotal role in modernising the ML portfolio in collaboration with a remarkable ML Ops team. Originally from Ireland and now based in London, Michelle blends her technical expertise with a love for the arts. Andrew Baker Andrew graduated from the University of Birmingham with a first-class honours degree in Mathematics and Music with a Year in Computer Science and joined Lloyds Banking Group on their Retail graduate scheme in 2015. Since 2021 Andrew has worked in the world of data, firstly in shaping the Retail data strategy and most recently as a Data Science Delivery Lead, growing and managing a team of Data Scientists and Machine Learning Engineers. He has built a high-performing team responsible for building and maintaining ML models in production for the Consumer Lending division of the bank. Andrew is motivated by the role that data science and ML can play in transforming the business and its processes, and is focused on balancing the power of ML with the need for simplicity and explainability that enables business users to engage with the opportunities that exist in this space and the demands of a highly regulated environment. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: https://www.michelleconway.co.uk/ https://www.linkedin.com/pulse/artificial-intelligence-just-when-data-science-answer-andrew-baker-hfdge/ https://www.linkedin.com/pulse/artificial-intelligence-conundrum-generative-ai-andrew-baker-qla7e/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Michelle on LinkedIn: https://www.linkedin.com/in/michelle--conway/ Connect with Andrew on LinkedIn: https://www.linkedin.com/in/andrew-baker-90952289…

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Holistic Evaluation of Generative AI Systems // Jineet Doshi // #280 57:33

30 weeks پیش57:33

57:33

Jineet Doshi is an award-winning Scientist, Machine Learning Engineer, and Leader at Intuit with over 7 years of experience. He has a proven track record of leading successful AI projects and building machine-learning models from design to production across various domains which have impacted 100 million customers and significantly improved business metrics, leading to millions of dollars of impact. Holistic Evaluation of Generative AI Systems // MLOps Podcast #280 with Jineet Doshi, Staff AI Scientist or AI Lead at Intuit. // Abstract Evaluating LLMs is essential in establishing trust before deploying them to production. Even post deployment, evaluation is essential to ensure LLM outputs meet expectations, making it a foundational part of LLMOps. However, evaluating LLMs remains an open problem. Unlike traditional machine learning models, LLMs can perform a wide variety of tasks such as writing poems, Q&A, summarization etc. This leads to the question how do you evaluate a system with such broad intelligence capabilities? This talk covers the various approaches for evaluating LLMs such as classic NLP techniques, red teaming and newer ones like using LLMs as a judge, along with the pros and cons of each. The talk includes evaluation of complex GenAI systems like RAG and Agents. It also covers evaluating LLMs for safety and security and the need to have a holistic approach for evaluating these very capable models. // Bio Jineet Doshi is an award winning AI Lead and Engineer with over 7 years of experience. He has a proven track record of leading successful AI projects and building machine learning models from design to production across various domains, which have impacted millions of customers and have significantly improved business metrics, leading to millions of dollars of impact. He is currently an AI Lead at Intuit where he is one of the architects and developers of their Generative AI platform, which is serving Generative AI experiences for more than 100 million customers around the world. Jineet is also a guest lecturer at Stanford University as part of their building LLM Applications class. He is on the Advisory Board of University of San Francisco’s AI Program. He holds multiple patents in the field, is on the steering committee of MLOps World Conference and has also co chaired workshops at top AI conferences like KDD. He holds a Masters degree from Carnegie Mellon university. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: https://www.intuit.com/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Jineet on LinkedIn: https://www.linkedin.com/in/jineetdoshi/…

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Unleashing Unconstrained News Knowledge Graphs to Combat Misinformation // Robert Caulk // #279 1:15:24

30 weeks پیش1:15:24

1:15:24

Robert Caulk is responsible for directing software development, enabling research, coordinating company projects, quality control, proposing external collaborations, and securing funding. He believes firmly in open-source, having spent 12 years accruing over 1000 academic citations building open-source software in domains such as machine learning, image analysis, and coupled physical processes. He received his Ph.D. from Université Grenoble Alpes, France, in computational mechanics. Unleashing Unconstrained News Knowledge Graphs to Combat Misinformation // MLOps Podcast #279 with Robert Caulk, Founder of Emergent Methods. // Abstract Indexing hundreds of thousands of news articles per day into a knowledge graph (KG) was previously impossible due to the strict requirement that high-level reasoning, general world knowledge, and full-text context *must* be present for proper KG construction. The latest tools now enable such general world knowledge and reasoning to be applied cost effectively to high-volumes of news articles. Beyond the low cost of processing these news articles, these tools are also opening up a new, controversial, approach to KG building - unconstrained KGs. We discuss the construction and exploration of the largest news-knowledge-graph on the planet - hosted on an endpoint at AskNews.app. During talk we aim to highlight some of the sacrifices and benefits that go hand-in-hand with using the infamous unconstrained KG approach. We conclude the talk by explaining how knowledge graphs like these help to mitigate misinformation. We provide some examples of how our clients are using this graph, such as generating sports forecasts, generating better social media posts, generating regional security alerts, and combating human trafficking. // Bio Robert is the founder of Emergent Methods, where he directs research and software development for large-scale applications. He is currently overseeing the structuring of hundreds of thousands of news articles per day in order to build the best news retrieval API in the world: https://asknews.app. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: https://emergentmethods.ai News Retrieval API: https://asknews.app --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Rob on LinkedIn: https://www.linkedin.com/in/rcaulk/ Timestamps: [00:00] Rob's preferred coffee [00:05] Takeaways [00:55] Please like, share, leave a review, and subscribe to our MLOps channels! [01:00] Join our Local Organizer Carousel! [02:15] Knowledge Graphs and ontology [07:43] Ontology vs Noun Approach [12:46] Ephemeral tools for efficiency [17:26] Oracle to PostgreSQL migration [22:20] MEM Graph life cycle [29:14] Knowledge Graph Investigation Insights [33:37] Fine-tuning and distillation of LLMs [39:28] DAG workflow and quality control [46:23] Crawling nodes with Phi 3 Llama [50:05] AI pricing risks and strategies [56:14] Data labeling and poisoning [58:34] API costs vs News latency [1:02:10] Product focus and value [1:04:52] Ensuring reliable information [1:11:01] Podcast transcripts as News [1:13:08] Ontology trade-offs explained [1:15:00] Wrap up…

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LLM Distillation and Compression // Guanhua Wang // #278 49:47

30 weeks پیش49:47

49:47

Guanhua Wang is a Senior Researcher in DeepSpeed Team at Microsoft . Before Microsoft , Guanhua earned his Computer Science PhD from UC Berkeley. Domino: Communication-Free LLM Training Engine // MLOps Podcast #278 with Guanhua "Alex" Wang, Senior Researcher at Microsoft. // Abstract Given the popularity of generative AI, Large Language Models (LLMs) often consume hundreds or thousands of GPUs to parallelize and accelerate the training process. Communication overhead becomes more pronounced when training LLMs at scale. To eliminate communication overhead in distributed LLM training, we propose Domino, which provides a generic scheme to hide communication behind computation. By breaking the data dependency of a single batch training into smaller independent pieces, Domino pipelines these independent pieces of training and provides a generic strategy of fine-grained communication and computation overlapping. Extensive results show that compared with Megatron-LM, Domino achieves up to 1.3x speedup for LLM training on Nvidia DGX-H100 GPUs. // Bio Guanhua Wang is a Senior Researcher in the DeepSpeed team at Microsoft. His research focuses on large-scale LLM training and serving. Previously, he led the ZeRO++ project at Microsoft which helped reduce over half of model training time inside Microsoft and Linkedin. He also led and was a major contributor to Microsoft Phi-3 model training. He holds a CS PhD from UC Berkeley advised by Prof Ion Stoica. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: https://guanhuawang.github.io/ DeepSpeed hiring: https://www.microsoft.com/en-us/research/project/deepspeed/opportunities/ Large Model Training and Inference with DeepSpeed // Samyam Rajbhandari // LLMs in Prod Conference: https://youtu.be/cntxC3g22oU --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Guanhua on LinkedIn: https://www.linkedin.com/in/guanhua-wang/ Timestamps: [00:00] Guanhua's preferred coffee [00:17] Takeaways [01:36] Please like, share, leave a review, and subscribe to our MLOps channels! [01:47] Phi model explanation [06:29] Small Language Models optimization challenges [07:29] DeepSpeed overview and benefits [10:58] Crazy unimplemented crazy AI ideas [17:15] Post training vs QAT [19:44] Quantization over distillation [24:15] Using Lauras [27:04] LLM scaling sweet spot [28:28] Quantization techniques [32:38] Domino overview [38:02] Training performance benchmark [42:44] Data dependency-breaking strategies [49:14] Wrap up…

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AI's Next Frontier // Aditya Naganath // #277 57:30

31 weeks پیش57:30

57:30

Thanks to the High Signal Podcast by Delphina: https://go.mlops.community/HighSignalPodcast Aditya Naganath is an experienced investor currently working with Kleiner Perkins . He has a passion for connecting with people over coffee and discussing various topics related to tech, products, ideas, and markets. AI's Next Frontier // MLOps Podcast #277 with Aditya Naganath, Principal at Kleiner Perkins. // Abstract LLMs have ushered in an unmistakable supercycle in the world of technology. The low-hanging use cases have largely been picked off. The next frontier will be AI coworkers who sit alongside knowledge workers, doing work side by side. At the infrastructure level, one of the most important primitives invented by man - the data center, is being fundamentally rethought in this new wave. // Bio Aditya Naganath joined Kleiner Perkins’ investment team in 2022 with a focus on artificial intelligence, enterprise software applications, infrastructure and security. Prior to joining Kleiner Perkins, Aditya was a product manager at Google focusing on growth initiatives for the next billion users team. He previously was a technical lead at Palantir Technologies and formerly held software engineering roles at Twitter and Nextdoor, where he was a Kleiner Perkins fellow. Aditya earned a patent during his time at Twitter for a technical analytics product he co-created. Originally from Mumbai India, Aditya graduated magna cum laude from Columbia University with a bachelor’s degree in Computer Science, and an MBA from Stanford University. Outside of work, you can find him playing guitar with a hard rock band, competing in chess or on the squash courts, and fostering puppies. He is also an avid poker player. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Faith's Hymn by Beautiful Chorus: ⁠ ⁠https://open.spotify.com/track/1bDv6grQB5ohVFI8UDGvKK?si=4b00752eaa96413b⁠ ⁠ Substack: ⁠ ⁠https://adityanaganath.substack.com/?utm_source=substack&utm_medium=web&utm_campaign=substack_profile⁠ ⁠ With thanks to the High Signal Podcast by Delphina: https://go.mlops.community/HighSignalPodcast Building the Future of AI in Software Development // Varun Mohan // MLOps Podcast #195 - ⁠ ⁠https://youtu.be/1DJKq8StuTo⁠ ⁠ Do Re MI for Training Metrics: Start at the Beginning // Todd Underwood // AIQCON - ⁠ ⁠https://youtu.be/DxyOlRdCofo --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Aditya on LinkedIn: https://www.linkedin.com/in/aditya-naganath/…

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PyTorch for Control Systems and Decision Making // Vincent Moens // #276 56:39

32 weeks پیش56:39

56:39

Dr Vincent Moens is an Applied Machine Learning Research Scientist at Meta and an author of TorchRL and TensorDict in Pytorch. PyTorch for Control Systems and Decision Making // MLOps Podcast #276 with Vincent Moens, Research Engineer at Meta. // Abstract PyTorch is widely adopted across the machine learning community for its flexibility and ease of use in applications such as computer vision and natural language processing. However, supporting reinforcement learning, decision-making, and control communities is equally crucial, as these fields drive innovation in areas like robotics, autonomous systems, and game-playing. This podcast explores the intersection of PyTorch and these fields, covering practical tips and tricks for working with PyTorch, an in-depth look at TorchRL, and discussions on debugging techniques, optimization strategies, and testing frameworks. By examining these topics, listeners will understand how to effectively use PyTorch for control systems and decision-making applications. // Bio Vincent Moens is a research engineer on the PyTorch core team at Meta, based in London. As the maintainer of TorchRL ( https://github.com/pytorch/rl ) and TensorDict ( https://github.com/pytorch/tensordict ), Vincent plays a key role in supporting the decision-making community within the PyTorch ecosystem. Alongside his technical role in the PyTorch community, Vincent also actively contributes to AI-related research projects. Before joining Meta, Vincent worked as an ML researcher at Huawei and AIG. Vincent holds a Medical Degree and a PhD in Computational Neuroscience. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Musical recommendation: https://open.spotify.com/artist/1Uff91EOsvd99rtAupatMP?si=jVkoFiq8Tmq0fqK_OIEglg Website: github.com/vmoens TorchRL: https://github.com/pytorch/rl TensorDict: https://github.com/pytorch/tensordict LinkedIn post: https://www.linkedin.com/posts/vincent-moens-9bb91972_join-the-tensordict-discord-server-activity-7189297643322253312-Wo9J?utm_source=share&utm_medium=member_desktop --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vincent on LinkedIn: https://www.linkedin.com/in/mvi/…

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AI-Driven Code: Navigating Due Diligence & Transparency in MLOps // Matt van Itallie // #275 57:01

33 weeks پیش57:01

57:01

Matt Van Itallie is the founder and CEO of Sema . Prior to this, they were the Vice President of Customer Support and Customer Operations at Social Solutions. AI-Driven Code: Navigating Due Diligence & Transparency in MLOps // MLOps Podcast #275 with Matt van Itallie, Founder and CEO of Sema. // Abstract Matt Van Itallie, founder and CEO of Sema, discusses how comprehensive codebase evaluations play a crucial role in MLOps and technical due diligence. He highlights the impact of Generative AI on code transparency and explains the Generative AI Bill of Materials (GBOM), which helps identify and manage risks in AI-generated code. This talk offers practical insights for technical and non-technical audiences, showing how proper diligence can enhance value and mitigate risks in machine learning operations. // Bio Matt Van Itallie is the Founder and CEO of Sema. He and his team have developed Comprehensive Codebase Scans, the most thorough and easily understandable assessment of a codebase and engineering organization. These scans are crucial for private equity and venture capital firms looking to make informed investment decisions. Sema has evaluated code within organizations that have a collective value of over $1 trillion. In 2023, Sema served 7 of the 9 largest global investors, along with market-leading strategic investors, private equity, and venture capital firms, providing them with critical insights. In addition, Sema is at the forefront of Generative AI Code Transparency, which measures how much code created by GenAI is in a codebase. They are the inventors behind the Generative AI Bill of Materials (GBOM), an essential resource for investors to understand and mitigate risks associated with AI-generated code. Before founding Sema, Matt was a Private Equity operating executive and a management consultant at McKinsey. He graduated from Harvard Law School and has had some interesting adventures, like hiking a third of the Appalachian Trail and biking from Boston to Seattle. Full bio: https://alistar.fm/bio/matt-van-itallie // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: https://en.m.wikipedia.org/wiki/Michael_Gschwind --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Matt on LinkedIn: https://www.linkedin.com/in/mvi/…

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PyTorch's Combined Effort in Large Model Optimization // Michael Gschwind // #274 57:44

34 weeks پیش57:44

57:44

Dr. Michael Gschwind is a Director / Principal Engineer for PyTorch at Meta Platforms . At Meta , he led the rollout of GPU Inference for production services. // MLOps Podcast #274 with Michael Gschwind, Software Engineer, Software Executive at Meta Platforms. // Abstract Explore the role in boosting model performance, on-device AI processing, and collaborations with tech giants like ARM and Apple. Michael shares his journey from gaming console accelerators to AI, emphasizing the power of community and innovation in driving advancements. // Bio Dr. Michael Gschwind is a Director / Principal Engineer for PyTorch at Meta Platforms. At Meta, he led the rollout of GPU Inference for production services. He led the development of MultiRay and Textray, the first deployment of LLMs at a scale exceeding a trillion queries per day shortly after its rollout. He created the strategy and led the implementation of PyTorch donation optimization with Better Transformers and Accelerated Transformers, bringing Flash Attention, PT2 compilation, and ExecuTorch into the mainstream for LLMs and GenAI models. Most recently, he led the enablement of large language models on-device AI with mobile and edge devices. // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://en.m.wikipedia.org/wiki/Michael_Gschwind --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Michael on LinkedIn: https://www.linkedin.com/in/michael-gschwind-3704222/?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=ios_app Timestamps: [00:00] Michael's preferred coffee [00:21] Takeaways [01:59] Please like, share, leave a review, and subscribe to our MLOps channels! [02:10] Gaming to AI Accelerators [11:34] Torch Chat goals [18:53] Pytorch benchmarking and competitiveness [21:28] Optimizing MLOps models [24:52] GPU optimization tips [29:36] Cloud vs On-device AI [38:22] Abstraction across devices [42:29] PyTorch developer experience [45:33] AI and MLOps-related antipatterns [48:33] When to optimize [53:26] Efficient edge AI models [56:57] Wrap up…

MLOps.community

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LLMs to agents: The Beauty & Perils of Investing in GenAI // VC Panel // Agents in Production 33:24

34 weeks پیش33:24

33:24

//Abstract In this segment, the Panel will dive into the evolving landscape of AI, where large language models (LLMs) power the next wave of intelligent agents. In this engaging panel, leading investors Meera (Redpoint), George (Sequoia), and Sandeep (Prosus Ventures) discuss the promise and pitfalls of AI in production. From transformative industry applications to the challenges of scalability, costs, and shifting business models, this session unpacks the metrics and insights shaping GenAI's future. Whether you're excited about AI's potential or wary of its complexities, this is a must-watch for anyone exploring the cutting edge of tech investment. //Bio Host: Paul van der Boor Senior Director Data Science @ Prosus Group Sandeep Bakshi Head of Investments, Europe @ Prosus Meera Clark Principal @ Redpoint Ventures George Robson Partner @ Sequoia Capital A Prosus | MLOps Community Production…

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We Can All Be AI Engineers and We Can Do It with Open Source Models // Luke Marsden // #273 51:08

34 weeks پیش51:08

51:08

Luke Marsden , is a passionate technology leader. Experienced in consultant, CEO, CTO, tech lead, product, sales, and engineering roles. Proven ability to conceive and execute a product vision from strategy to implementation, while iterating on product-market fit. We Can All Be AI Engineers and We Can Do It with Open Source Models // MLOps Podcast #273 with Luke Marsden, CEO of HelixML. // Abstract In this podcast episode, Luke Marsden explores practical approaches to building Generative AI applications using open-source models and modern tools. Through real-world examples, Luke breaks down the key components of GenAI development, from model selection to knowledge and API integrations, while highlighting the data privacy advantages of open-source solutions. // Bio Hacker & entrepreneur. Founder at helix.ml. Career spanning DevOps, MLOps, and now LLMOps. Working on bringing business value to local, open-source LLMs. // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://helix.ml About open source AI: https://blog.helix.ml/p/the-open-source-ai-revolution Ratatat Cream on Chrome: https://open.spotify.com/track/3s25iX3minD5jORW4KpANZ?si=719b715154f64a5f --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Luke on LinkedIn: https://www.linkedin.com/in/luke-marsden-71b3789/…

MLOps.community

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Exploring AI Agents: Voice, Visuals, and Versatility // Panel // Agents in Production 28:58

35 weeks پیش28:58

28:58

//Abstract This panel speaks about the diverse landscape of AI agents, focusing on how they integrate voice interfaces, GUIs, and small language models to enhance user experiences. They'll also examine the roles of these agents in various industries, highlighting their impact on productivity, creativity, and user experience and how these empower developers to build better solutions while addressing challenges like ensuring consistent performance and reliability across different modalities when deploying AI agents in production. //Bio Host: Diego Oppenheimer Co-founder @ Guardrails AI Jazmia Henry Founder and CEO @ Iso AI Rogerio Bonatti Researcher @ Microsoft Julia Kroll Applied Engineer @ Deepgram Joshua Alphonse Director of Developer Relations @ PremAI A Prosus | MLOps Community Production…

MLOps.community

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The Impact of UX Research in the AI Space // Lauren Kaplan // #272 1:08:19

35 weeks پیش1:08:19

1:08:19

Lauren Kaplan is a sociologist and writer. She earned her PhD in Sociology at Goethe University Frankfurt and worked as a researcher at the University of Oxford and UC Berkeley. The Impact of UX Research in the AI Space // MLOps Podcast #272 with Lauren Kaplan, Sr UX Researcher. // Abstract In this MLOps Community podcast episode, Demetrios and UX researcher Lauren Kaplan explore how UX research can transform AI and ML projects by aligning insights with business goals and enhancing user and developer experiences. Kaplan emphasizes the importance of stakeholder alignment, proactive communication, and interdisciplinary collaboration, especially in adapting company culture post-pandemic. They discuss UX’s growing relevance in AI, challenges like bias, and the use of AI in research, underscoring the strategic value of UX in driving innovation and user satisfaction in tech. // Bio Lauren is a sociologist and writer. She earned her PhD in Sociology at Goethe University Frankfurt and worked as a researcher at the University of Oxford and UC Berkeley. Passionate about homelessness and Al, Lauren joined UCSF and later Meta. Lauren recently led UX research at a global Al chip startup and is currently seeking new opportunities to further her work in UX research and AI. At Meta, Lauren led UX research for 1) Privacy-Preserving ML and 2) PyTorch. Lauren has worked on NLP projects such as Word2Vec analysis of historical HIV/AIDS documents presented at TextXD, UC Berkeley 2019. Lauren is passionate about understanding technology and advocating for the people who create and consume Al. Lauren has published over 30 peer-reviewed research articles in domains including psychology, medicine, sociology, and more.” // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Podcast on AI UX https://open.substack.com/pub/aistudios/p/how-to-do-user-research-for-ai-products?r=7hrv8&utm_medium=ios 2024 State of AI Infra at Scale Research Report https://ai-infrastructure.org/wp-content/uploads/2024/03/The-State-of-AI-Infrastructure-at-Scale-2024.pdf Privacy-Preserving ML UX Public Article https://www.ttclabs.net/research/how-to-help-people-understand-privacy-enhancing-technologies Homelessness research and more: https://scholar.google.com/citations?user=24zqlwkAAAAJ&hl=en Agents in Production: https://home.mlops.community/public/events/aiagentsinprod Mk.gee Si (Bonus Track): https://open.spotify.com/track/1rukW2Wxnb3GGlY0uDWIWB?si=4d5b0987ad55444a --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Lauren on LinkedIn: https://www.linkedin.com/in/laurenmichellekaplan?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=ios_app…

MLOps.community

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EU AI Act - Navigating New Legislation // Petar Tsankov // MLOps Podcast #271 58:56

37 weeks پیش58:56

58:56

Dr. Petar Tsankov is a researcher and entrepreneur in the field of Computer Science and Artificial Intelligence (AI). EU AI Act - Navigating New Legislation // MLOps Podcast #271 with Petar Tsankov, Co-Founder and CEO of LatticeFlow AI. Big thanks to LatticeFlow for sponsoring this episode! // Abstract Dive into AI risk and compliance. Petar Tsankov, a leader in AI safety, talks about turning complex regulations into clear technical requirements and the importance of benchmarks in AI compliance, especially with the EU AI Act. We explore his work with big AI players and the EU on safer, compliant models, covering topics from multimodal AI to managing AI risks. He also shares insights on "Comply," an open-source tool for checking AI models against EU standards, making compliance simpler for AI developers. A must-listen for those tackling AI regulation and safety. // Bio Co-founder & CEO at LatticeFlow AI, building the world's first product enabling organizations to build performant, safe, and trustworthy AI systems. Before starting LatticeFlow AI, Petar was a senior researcher at ETH Zurich working on the security and reliability of modern systems, including deep learning models, smart contracts, and programmable networks. Petar have co-created multiple publicly available security and reliability systems that are regularly used: = ERAN, the world's first scalable verifier for deep neural networks: https://github.com/eth-sri/eran = VerX, the world's first fully automated verifier for smart contracts: https://verx.ch = Securify, the first scalable security scanner for Ethereum smart contracts: https://securify.ch = DeGuard, de-obfuscates Android binaries: http://apk-deguard.com = SyNET, the first scalable network-wide configuration synthesis tool: https://synet.ethz.ch Petar also co-founded ChainSecurity, an ETH spin-off that within 2 years became a leader in formal smart contract audits and was acquired by PwC Switzerland in 2020. // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://latticeflow.ai/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Petar on LinkedIn: https://www.linkedin.com/in/petartsankov/…

MLOps.community

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Boosting LLM/RAG Workflows & Scheduling w/ Composable Memory and Checkpointing // Bernie Wu // #270 55:18

38 weeks پیش55:18

55:18

Bernie Wu is VP of Business Development for MemVerge . He has 25+ years of experience as a senior executive for data center hardware and software infrastructure companies including companies such as Conner/Seagate, Cheyenne Software, Trend Micro, FalconStor, Levyx, and MetalSoft. Boosting LLM/RAG Workflows & Scheduling w/ Composable Memory and Checkpointing // MLOps Podcast #270 with Bernie Wu, VP Strategic Partnerships/Business Development of MemVerge. // Abstract Limited memory capacity hinders the performance and potential of research and production environments utilizing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques. This discussion explores how leveraging industry-standard CXL memory can be configured as a secondary, composable memory tier to alleviate this constraint. We will highlight some recent work we’ve done in integrating of this novel class of memory into LLM/RAG/vector database frameworks and workflows. Disaggregated shared memory is envisioned to offer high performance, low latency caches for model/pipeline checkpoints of LLM models, KV caches during distributed inferencing, LORA adaptors, and in-process data for heterogeneous CPU/GPU workflows. We expect to showcase these types of use cases in the coming months. // Bio Bernie is VP of Strategic Partnerships/Business Development for MemVerge. His focus has been building partnerships in the AI/ML, Kubernetes, and CXL memory ecosystems. He has 25+ years of experience as a senior executive for data center hardware and software infrastructure companies including companies such as Conner/Seagate, Cheyenne Software, Trend Micro, FalconStor, Levyx, and MetalSoft. He is also on the Board of Directors for Cirrus Data Solutions. Bernie has a BS/MS in Engineering from UC Berkeley and an MBA from UCLA. // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.memverge.com Accelerating Data Retrieval in Retrieval Augmentation Generation (RAG) Pipelines using CXL: https://memverge.com/accelerating-data-retrieval-in-rag-pipelines-using-cxl/ Do Re MI for Training Metrics: Start at the Beginning // Todd Underwood // AIQCON: https://youtu.be/DxyOlRdCofo Handling Multi-Terabyte LLM Checkpoints // Simon Karasik // MLOps Podcast #228: https://youtu.be/6MY-IgqiTpg Compute Express Link (CXL) FPGA IP: https://www.intel.com/content/www/us/en/products/details/fpga/intellectual-property/interface-protocols/cxl-ip.htmlUltra Ethernet Consortium: https://ultraethernet.org/ Unified Acceleration (UXL) Foundation: https://www.intel.com/content/www/us/en/developer/articles/news/unified-acceleration-uxl-foundation.html RoCE networks for distributed AI training at scale: https://engineering.fb.com/2024/08/05/data-center-engineering/roce-network-distributed-ai-training-at-scale/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Bernie on LinkedIn: https://www.linkedin.com/in/berniewu/ Timestamps: [00:00] Bernie's preferred coffee [00:11] Takeaways [01:37] First principles thinking focus [05:02] Memory Abundance Concept Discussion [06:45] Managing load spikes [09:38] GPU checkpointing challenges [16:29] Distributed memory problem solving [18:27] Composable and Virtual Memory [21:49] Interactive chat annotation [23:46] Memory elasticity in AI [27:33] GPU networking tests [29:12] GPU Scheduling workflow optimization [32:18] Kubernetes Extensions and Tools [37:14] GPU bottleneck analysis [42:04] Economical memory strategies [45:14] Elastic memory management strategies [47:57] Problem solving approach [50:15] AI infrastructure elasticity evolution [52:33] RDMA and RoCE explained [54:14] Wrap up…

MLOps.community

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How to Systematically Test and Evaluate Your LLMs Apps // Gideon Mendels // #269 1:01:42

39 weeks پیش1:01:42

1:01:42

Gideon Mendels is the Chief Executive Officer at Comet , the leading solution for managing machine learning workflows. How to Systematically Test and Evaluate Your LLMs Apps // MLOps Podcast #269 with Gideon Mendels, CEO of Comet. // Abstract When building LLM Applications, Developers need to take a hybrid approach from both ML and SW Engineering best practices. They need to define eval metrics and track their entire experimentation to see what is and is not working. They also need to define comprehensive unit tests for their particular use-case so they can confidently check if their LLM App is ready to be deployed. // Bio Gideon Mendels is the CEO and co-founder of Comet, the leading solution for managing machine learning workflows from experimentation to production. He is a computer scientist, ML researcher and entrepreneur at his core. Before Comet, Gideon co-founded GroupWize, where they trained and deployed NLP models processing billions of chats. His journey with NLP and Speech Recognition models began at Columbia University and Google where he worked on hate speech and deception detection. // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.comet.com/site/ All the Hard Stuff with LLMs in Product Development // Phillip Carter // MLOps Podcast #170: https://youtu.be/DZgXln3v85s Opik by Comet: https://www.comet.com/site/products/opik/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Gideon on LinkedIn: https://www.linkedin.com/in/gideon-mendels/ Timestamps: [00:00] Gideon's preferred coffee [00:17] Takeaways [01:50] A huge shout-out to Comet ML for sponsoring this episode! [02:09] Please like, share, leave a review, and subscribe to our MLOps channels! [03:30] Evaluation metrics in AI [06:55] LLM Evaluation in Practice [10:57] LLM testing methodologies [16:56] LLM as a judge [18:53] OPIC track function overview [20:33] Tracking user response value [26:32] Exploring AI metrics integration [29:05] Experiment tracking and LLMs [34:27] Micro Macro collaboration in AI [38:20] RAG Pipeline Reproducibility Snapshot [40:15] Collaborative experiment tracking [45:29] Feature flags in CI/CD [48:55] Labeling challenges and solutions [54:31] LLM output quality alerts [56:32] Anomaly detection in model outputs [1:01:07] Wrap up…

MLOps.community

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Exploring the Impact of Agentic Workflows // Raj Rikhy // #268 51:02

39 weeks پیش51:02

51:02

Raj Rikhy is a Senior Product Manager at Microsoft AI + R, enabling deep reinforcement learning use cases for autonomous systems. Previously, Raj was the Group Technical Product Manager in the CDO for Data Science and Deep Learning at IBM. Prior to joining IBM, Raj has been working in product management for several years - at Bitnami, Appdirect and Salesforce. // MLOps Podcast #268 with Raj Rikhy, Principal Product Manager at Microsoft. // Abstract In this MLOps Community podcast, Demetrios chats with Raj Rikhy, Principal Product Manager at Microsoft, about deploying AI agents in production. They discuss starting with simple tools, setting clear success criteria, and deploying agents in controlled environments for better scaling. Raj highlights real-time uses like fraud detection and optimizing inference costs with LLMs, while stressing human oversight during early deployment to manage LLM randomness. The episode offers practical advice on deploying AI agents thoughtfully and efficiently, avoiding over-engineering, and integrating AI into everyday applications. // Bio Raj is a Senior Product Manager at Microsoft AI + R, enabling deep reinforcement learning use cases for autonomous systems. Previously, Raj was the Group Technical Product Manager in the CDO for Data Science and Deep Learning at IBM. Prior to joining IBM, Raj has been working in product management for several years - at Bitnami, Appdirect and Salesforce. // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.microsoft.com/en-us/research/focus-area/ai-and-microsoft-research/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Raj on LinkedIn: https://www.linkedin.com/in/rajrikhy/…

MLOps.community

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The Only Constant is (Data) Change // Panel // DE4AI 40:49

40 weeks پیش40:49

40:49

//Abstract If there is one thing that is true, it is data is constantly changing. How can we keep up with these changes? How can we make sure that every stakeholder has visibility? How can we create a culture of understanding around data change management? //Bio - Benjamin Rogojan: Data Science And Engineering Consultant @ Seattle Data Guy - Chad Sanderson: CEO & Co-Founder @ Gable - Christophe Blefari: CTO & Co-founder @ NAO - Maggie Hays: Founding Community Product Manager, DataHub @ Acryl Data A big thank you to our Premium Sponsors @Databricks , @tecton8241 , & @onehouseHQ for their generous support!…

MLOps.community

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The AI Dream Team: Strategies for ML Recruitment and Growth // Jelmer Borst and Daniela Solis // #267 58:42

40 weeks پیش58:42

58:42

The AI Dream Team: Strategies for ML Recruitment and Growth // MLOps Podcast #267 with Jelmer Borst, Analytics & Machine Learning Domain Lead, and Daniela Solis, Machine Learning Product Owner, of Picnic. // Abstract Like many companies, Picnic started out with a small, central data science team. As this grows larger, focussing on more complex models, it questions the skillsets & organisational set up. Use an ML platform, or build ourselves? A central team vs. embedded? Hire data scientists vs. ML engineers vs. MLOps engineers How to foster a team culture of end-to-end ownership How to balance short-term & long-term impact // Bio Jelmer Borst Jelmer leads the analytics & machine learning teams at Picnic, an app-only online groceries company based in The Netherlands. Whilst his background is in aerospace engineering, he was looking for something faster-paced and found that at Picnic. He loves the intersection of solving business challenges using technology & data. In his free time loves to cook food and tinker with the latest AI developments. Daniela Solis Morales As a Machine Learning Lead at Picnic, I am responsible for ensuring the success of end-to-end Machine Learning systems. My work involves bringing models into production across various domains, including Personalization, Fraud Detection, and Natural Language Processing. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Jelmer on LinkedIn: https://www.linkedin.com/in/japborst Connect with Daniela on LinkedIn: https://www.linkedin.com/in/daniela-solis-morales/…

MLOps.community

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Making Your Company LLM-native // Francisco Ingham // #266 57:54

41 weeks پیش57:54

57:54

Francisco Ingham , LLM consultant, NLP developer, and founder of Pampa Labs . Making Your Company LLM-native // MLOps Podcast #266 with Francisco Ingham, Founder of Pampa Labs. // Abstract Being an LLM-native is becoming one of the key differentiators among companies, in vastly different verticals. Everyone wants to use LLMs, and everyone wants to be on top of the current tech but - what does it really mean to be LLM-native? LLM-native involves two ends of a spectrum. On the one hand, we have the product or service that the company offers, which surely offers many automation opportunities. LLMs can be applied strategically to scale at a lower cost and offer a better experience for users. But being LLM-native not only involves the company's customers, it also involves each stakeholder involved in the company's operations. How can employees integrate LLMs into their daily workflows? How can we as developers leverage the advancements in the field not only as builders but as adopters? We will tackle these and other key questions for anyone looking to capitalize on the LLM wave, prioritizing real results over the hype. // Bio Currently working at Pampa Labs, where we help companies become AI-native and build AI-native products. Our expertise lies on the LLM-science side, or how to build a successful data flywheel to leverage user interactions to continuously improve the product. We also spearhead, pampa-friends - the first Spanish-speaking community of AI Engineers. Previously worked in management consulting, was a TA in fastai in SF, and led the cross-AI + dev tools team at Mercado Libre. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: pampa.ai --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Francisco on LinkedIn: https://www.linkedin.com/in/fpingham/ Timestamps: [00:00] Francisco's preferred coffee [00:13] Takeaways [00:37] Please like, share, leave a review, and subscribe to our MLOps channels! [00:51] A Literature Geek [02:41] LLM-native company [03:54] Integrating LLM in workflows [07:21] Unexpected LLM applications [10:38] LLM's in development process [14:00] Vibe check to evaluation [15:36] Experiment tracking optimizations [20:22] LLMs as judges discussion [24:43] Presentaciones automatizadas para podcast [27:48] AI operating system and agents [31:29] Importance of SEO expertise [35:33] Experimentation and evaluation [39:20] AI integration strategies [41:50] RAG approach spectrum analysis [44:40] Search vs Retrieval in AI [49:02] Recommender Systems vs RAG [52:08] LLMs in recommender systems [53:10] LLM interface design insights…

MLOps.community

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Unpacking 3 Types of Feature Stores // Simba Khadder // #265 1:07:42

42 weeks پیش1:07:42

1:07:42

Simba Khadder is the Founder & CEO of Featureform . He started his ML career in recommender systems where he architected a multi-modal personalization engine that powered 100s of millions of user’s experiences. Unpacking 3 Types of Feature Stores // MLOps Podcast #265 with Simba Khadder, Founder & CEO of Featureform. // Abstract Simba dives into how feature stores have evolved and how they now intersect with vector stores, especially in the world of machine learning and LLMs. He breaks down what embeddings are, how they power recommender systems, and why personalization is key to improving LLM prompts. Simba also sheds light on the difference between feature and vector stores, explaining how each plays its part in making ML workflows smoother. Plus, we get into the latest challenges and cool innovations happening in MLOps. // Bio Simba Khadder is the Founder & CEO of Featureform. After leaving Google, Simba founded his first company, TritonML. His startup grew quickly and Simba and his team built ML infrastructure that handled over 100M monthly active users. He instilled his learnings into Featureform’s virtual feature store. Featureform turns your existing infrastructure into a Feature Store. He’s also an avid surfer, a mixed martial artist, a published astrophysicist for his work on finding Planet 9, and he ran the SF marathon in basketball shoes. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: featureform.comBigQuery Feature Store // Nicolas Mauti // MLOps Podcast #255: https://www.youtube.com/watch?v=NtDKbGyRHXQ&ab_channel=MLOps.community --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Simba on LinkedIn: https://www.linkedin.com/in/simba-k/ Timestamps: [00:00] Simba's preferred coffee [00:08] Takeaways [02:01] Coining the term 'Embedding' [07:10] Dual Tower Recommender System [10:06] Complexity vs Reliability in AI [12:39] Vector Stores and Feature Stores [17:56] Value of Data Scientists [20:27] Scalability vs Quick Solutions [23:07] MLOps vs LLMOps Debate [24:12] Feature Stores' current landscape [32:02] ML lifecycle challenges and tools [36:16] Feature Stores bundling impact [42:13] Feature Stores and BigQuery [47:42] Virtual vs Literal Feature Store [50:13] Hadoop Community Challenges [52:46] LLM data lifecycle challenges [56:30] Personalization in prompting usage [59:09] Contextualizing company variables [1:03:10] DSPy framework adoption insights [1:05:25] Wrap up…

MLOps.community

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Reinvent Yourself and Be Curious // Stefano Bosisio // MLOps Podcast #264 57:15

42 weeks پیش57:15

57:15

Stefano Bosisio is an accomplished MLOps Engineer with a solid background in Biomedical Engineering, focusing on cellular biology, genetics, and molecular simulations. Reinvent Yourself and Be Curious // MLOps Podcast #264 with Stefano Bosisio, MLOps Engineer at Synthesia. // Abstract This talk goes through Stefano's experience, to be an inspirational source for whoever wants to jump on a career in the MLOps sector. Moreover, Stefano will also introduce his MLOps Course on the MLOps community platform. // Bio Sai Bharath Gottam Stefano Bosisio is an MLOps Engineer, with a versatile background that ranges from biomedical engineering to computational chemistry and data science. Stefano got an MSc in biomedical engineering from the Polytechnic of Milan, focusing on cellular biology, genetics, and molecular simulations. Then, he landed in Scotland, in Edinburgh, to earn a PhD in chemistry from the University of Edinburgh, where he developed robust physical theories and simulation methods, to understand and unlock the drug discovery problem. After completing his PhD, Stefano transitioned into Data Science, where he began his career as a data scientist. His interest in machine learning engineering grew, leading him to specialize in building ML platforms that drive business success. Stefano's expertise bridges the gap between complex scientific research and practical machine learning applications, making him a key figure in the MLOps field. Bonus points beyond data: Stefano, as a proper Italian, loves cooking and (mainly) baking, playing the piano, crocheting and running half-marathons. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://medium.com/@stefanobosisio1First MLOps Stack Course: https://learn.mlops.community/courses/languages/your-first-mlops-stack/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Stefano on LinkedIn: https://www.linkedin.com/in/stefano-bosisio1/ Timestamps: [00:00] Stephano's preferred coffee [00:12] Takeaways [01:06] Stephano's MLOps Course [01:47] From Academia to AI Industry [09:10] Data science and platforms [16:53] Persistent MLOps challenges [21:23] Internal evangelization for success [24:21] Adapt communication skills to diverse individual needs [29:43] Key components of ML pipelines are essentia l[33:47] Create a generalizable AI training pipeline with Kubeflow [35:44] Consider cost-effective algorithms and deployment methods [39:02] Agree with dream platform; LLMs require simple microservice [42:48] Auto scaling: crucial, tricky, prone to issues [46:28] Auto-scaling issues with Apache Beam data pipelines [49:49] Guiding students through MLOps with practical experience [53:16] Bulletproof Problem Solving: Decision trees for problem analysis [55:03] Evaluate tools critically; appreciate educational opportunities [57:01] Wrap up…

MLOps.community

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Global Feature Store // Gottam Sai Bharath & Cole Bailey // #263 50:18

42 weeks پیش50:18

50:18

Global Feature Store: Optimizing Locally and Scaling Globally at Delivery Hero // MLOps Podcast #263 with Delivery Hero's Gottam Sai Bharath, Senior Machine Learning Engineer & Cole Bailey, ML Platform Engineering Manager. // Abstract Delivery Hero innovates locally within each department to develop MLOps practices most effective in that particular context. We also discuss our efforts to reduce redundancy and inefficiency across the company. Hear about our experiences in creating multiple micro feature stores within our departments, and our goal to unify these into a Global Feature Store that is more powerful when combined. // Bio Sai Bharath Gottam With a passion for translating complex technical concepts into practical solutions, Sai excels at making intricate topics accessible and engaging. As a Senior Machine Learning Engineer at Delivery Hero, Sai works on cutting-edge machine learning platforms that guarantee seamless delivery experiences. Always eager to share insights and innovations, Sai is committed to making technology understandable and enjoyable for all. Cole Bailey Bridging data science and production-grade software engineering. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.deliveryhero.com/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Sai on LinkedIn: https://www.linkedin.com/in/sai-bharath-gottam/ Connect with Cole on LinkedIn: www.linkedin.com/in/cole-bailey Timestamps: [00:00] Sai and Cole's preferred coffee [00:42] Takeaways [01:51] Please like, share, leave a review, and subscribe to our MLOps channels! [02:08] Life changes in Delivery Hero [05:21] Global Feature Store and Pandora [12:21] Tech integration strategies [20:08] Defining Feature and Feature Store [22:46] Feature Store vs Data Platform [26:26] Features are discoverable [32:56] Onboarding and Feature Testing [36:00] Data consistency [41:07] Future Vision Feature Store [44:17] Multi-cloud strategies [46:33] Wrap up…

MLOps.community

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RAG Quality Starts with Data Quality // Adam Kamor // #262 59:33

43 weeks پیش59:33

59:33

Adam Kamor is the Co-founder of Tonic , a company that specializes in creating mock data that preserves secure datasets. RAG Quality Starts with Data Quality // MLOps Podcast #262 with Adam Kamor, Co-Founder & Head of Engineering of Tonic.ai. // Abstract Dive into what makes Retrieval-Augmented Generation (RAG) systems tick—and it all starts with the data. We’ll be talking with an expert in the field who knows exactly how to transform messy, unstructured enterprise data into high-quality fuel for RAG systems. Expect to learn the essentials of data prep, uncover the common challenges that can derail even the best-laid plans, and discover some insider tips on how to boost your RAG system’s performance. We’ll also touch on the critical aspects of data privacy and governance, ensuring your data stays secure while maximizing its utility. If you’re aiming to get the most out of your RAG systems or just curious about the behind-the-scenes work that makes them effective, this episode is packed with insights that can help you level up your game. // Bio Adam Kamor, PhD, is the Co-founder and Head of Engineering of Tonic.ai. Since completing his PhD in Physics at Georgia Tech, Adam has committed himself to enabling the work of others through the programs he develops. In his roles at Microsoft and Kabbage, he handled UI design and led the development of new features to anticipate customer needs. At Tableau, he played a role in developing the platform’s analytics/calculation capabilities. As a founder of Tonic.ai, he is leading the development of unstructured data solutions that are transforming the work of fellow developers, analysts, and data engineers alike. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.tonic.ai Various topics about RAG and LLM security are available on Tonic.ai's blogs: https://www.tonic.ai/blog https://www.tonic.ai/blog/how-to-prevent-data-leakage-in-your-ai-applications-with-tonic-textual-and-snowpark-container-services https://www.tonic.ai/blog/rag-evaluation-series-validating-the-rag-performance-of-the-openais-rag-assistant-vs-googles-vertex-search-and-conversation https://www.youtube.com/watch?v=5xdyt4oRONU https://www.tonic.ai/blog/what-is-retrieval-augmented-generation-the-benefits-of-implementing-rag-in-using-llms --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Adam on LinkedIn: https://www.linkedin.com/in/adam-kamor-85720b48/ Timestamps: [00:00] Adam's preferred coffee [00:24] Takeaways [00:59] Huge shout out to Tonic.ai for supporting the community! [01:03] Please like, share, leave a review, and subscribe to our MLOps channels! [01:18] Naming a product [03:38] Tonic Textual [08:00] Managing PII and Data Safety [10:16] Chunking strategies for context [14:19] Data prep for RAG [17:20] Data quality in AI systems [20:58] Data integrity in PDFs [27:12] Ensuring chatbot data freshness [33:02] Managed PostgreSQL and Vector DB [34:49] RBAC database vs file access [37:35] Slack AI data leakage solutions [42:26] Hot swapping [46:06] LLM security concerns [47:03] Privacy management best practices [49:02] Chatbot design patterns [50:39] RAG growth and impact [52:40] Retrieval Evaluation best practices [59:20] Wrap up…

MLOps.community

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Who's MLOps for Anyway? // Jonathan Rioux // #261 1:10:14

43 weeks پیش1:10:14

1:10:14

Jonathan Rioux is a Managing Principal of AI Consulting for EPAM Systems , where he advises clients on how to get from idea to realized AI products with the minimum of fuss and friction. Who's MLOps for Anyway? // MLOps Podcast #261 with Jonathan Rioux, Managing Principal, AI Consulting at EPAM Systems. // Abstract The year is 2024 and we are all staring into the cliff towards the abyss of disillusionment for Generative AI. Every organization, developer, and AI-adjacent individual is now talking about "making AI real" and "turning a ROI on AI initiatives". MLOps and LLMOps are taking the stage as the solution; equip your AI teams with the best tools money can buy, grab tokens by the fistful, and look at value raking in. Sounds familiar and eerily similar to the previous ML hype cycles? From solo devs to large organizations, how can we avoid the same pitfalls as last time and get out of the endless hamster wheel? // Bio Jonathan is a Managing Principal of AI Consulting for EPAM, where he advises client on how to get from idea to realized AI products with the minimum of fuss and friction. He's obsessed with the mental models of ML and how to organize harmonious AI practices. Jonathan published "Data Analysis with Python and PySpark" (Manning, 2022). // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: raiks.ca --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Jonathan on LinkedIn: https://www.linkedin.com/in/jonathanrx/ Timestamps: [00:00] Jonathan's preferred coffee [00:25] Takeaways [01:44] MLOps as not being sexy [03:49] Do not conflate MLOps with ROI [06:21] ML Certification Business Idea [11:02] AI Adoption Missteps [15:40] Slack AI Privacy Risks [18:17] Decentralized AI success [22:00] Michelangelo Hub-Spoke Model [27:45] Engineering tools for everyone [33:38 - 35:20] SAS Ad [35:21] POC to ROI transition [42:08] Repurposing project learnings [46:24] Balancing Innovation and ROI [55:35] Using classification model [1:00:24] Chatbot evolution comparison [1:01:20] Balancing Automation and Trust [1:06:30] Manual to AI transition [1:09:57] Wrap up…

MLOps.community

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Alignment is Real // Shiva Bhattacharjee // #260 40:20

44 weeks پیش40:20

40:20

Shiva Bhattacharjee is the Co-founder and CTO of TrueLaw , where we are building bespoke models for law firms for a wide variety of tasks. Alignment is Real // MLOps Podcast #260 with Shiva Bhattacharjee, CTO of TrueLaw Inc. // Abstract If the off-the-shelf model can understand and solve a domain-specific task well enough, either your task isn't that nuanced or you have achieved AGI. We discuss when is fine-tuning necessary over prompting and how we have created a loop of sampling - collecting feedback - fine-tuning to create models that seem to perform exceedingly well in domain-specific tasks. // Bio 20 years of experience in distributed and data-intensive systems spanning work at Apple, Arista Networks, Databricks, and Confluent. Currently CTO at TrueLaw where we provide a framework to fold in user feedback, such as lawyer critiques of a given task, and fold them into proprietary LLM models through fine-tuning mechanics, resulting in 7-10x improvements over the base model. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.truelaw.ai --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Shiva on LinkedIn: https://www.linkedin.com/in/shivabhattacharjee/ Timestamps: [00:00] Shiva's preferred coffee [00:58] Takeaways [01:17] DSPy Implementation [04:57] Evaluating DSPy risks [08:13] Community-driven DSPy tool [12:19] RAG implementation strategies [17:02] Cost-effective embedding fine-tuning [18:51] AI infrastructure decision-making [24:13] Prompt data flow evolution [26:32] Buy vs build decision [30:45] Tech stack insights [38:20] Wrap up…

MLOps.community

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Ax a New Way to Build Complex Workflows with LLMs // Vikram Rangnekar // #259 53:25

44 weeks پیش53:25

53:25

Vikram Rangnekar is an open-source software developer focused on simplifying LLM integration. He created LLMClient, a TypeScript library inspired by Stanford's DSP paper. With years of experience building complex LLM workflows, he previously worked as a senior software engineer at LinkedIn on Ad Serving. Ax a New Way to Build Complex Workflows with LLMs // MLOps Podcast #259 with Vikram Rangnekar, Software Engineer at Stealth. // Abstract Ax is a new way to build complex workflows with LLMs. It's a typescript library based on research done in the Stanford DSP paper. Concepts such as prompt signatures, prompt tuning, and composable prompts help you build RAG and agent-powered ideas that have till now been hard to build and maintain. Ax is designed for production usage. // Bio Vikram builds open-source software. Currently working on making it easy to build with LLMs. Created Ax a typescript library that abstracts over all the complexity of LLMs, it is based on the research done in the Stanford DSP paper. Worked extensively with LLMs over the last few years to build complex workflows. Previously worked as a senior software engineer with LinkedIn on Ad Serving. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links The unofficial DSPy framework. Build LLM-powered Agents and "Agentic workflows" based on the Stanford DSP paper: https://axllm.dev All the Hard Stuff with LLMs in Product Development // Phillip Carter // MLOps Podcast #170: https://youtu.be/DZgXln3v85s --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vikram on LinkedIn: https://www.linkedin.com/in/vikramr Timestamps: [00:00] Vikram preferred coffee [00:41] Takeaways [01:05] Data Engineering for AI/ML Conference Ad [01:41] Vikram's work these days [04:54] Fine-tuned Model insights [06:22] Java Script tool evolution [16:14] DSP knowledge distillation [17:34] DSP vs Manual examples [22:53] Optimizing task context [27:58] API type validation explained [30:25] LLM value and innovation [34:22] Navigating complex systems [37:30] DSP code generators explained [40:56] Exploring LLM personas [42:45] Optimizing small agents [43:32] Complex task assistance [49:53] Wrap up…

MLOps.community

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Building in Production Human-centred GenAI Solutions // Mohamed Abusaid & Mara Pometti// #177 1:02:42

45 weeks پیش1:02:42

1:02:42

MLOps Coffee Sessions #177 with Mohamed Abusaid and Mara Pometti, Building in Production Human-centred GenAI Solutions sponsored by QuantumBlack, AI by McKinsey . // Abstract Trust is paramount in the adoption of new technologies, especially in the realm of education. Mohamed and Mara shed light on the importance of AI governance programs and establishing AI governance boards to ensure safe and ethical use of technology while managing associated risks. They discuss the impact on customers, potential risks, and mitigation strategies that organizations must consider to protect their brand reputation and comply with regulations. // Bio Mara Pometti Mara is an Associate Design Director at McKinsey & Company, where she helps organisations drive AI adoption through human-centered methods. She defines herself as a data-savvy humanist. Her practice spans across AI, data journalism, and design with the overarching objective of finding the strategic intersection between AI models and human intents to implement responsible AI systems that move organisations forward. Previously, she led the AI Strategy practice at IBM, where she also developed the company’s first-ever data storytelling program. Yet, by background, she is a data journalist. She worked as a data journalist for agencies and newsrooms like Aljazeera. Mara lectured at many universities about how to humanize AI, including the London School of Economics. Her books and writing explore how to weave a humanistic approach to AI development. Mohamed Abusaid Am Mohamed, a tech enthusiast, hacker, avid traveler, and foodie all rolled into one individual. Built his first website when he was 9 and fell in love with computers and the internet ever since. Graduated with computer science from university although dabbled in electrical, electronic, and network engineering before that. When he's not reading up on the latest tech conversations and products on Hacker News, Mohamed spends his time traveling to new destinations and exploring their cuisine and culture. Mohamed works with different companies helping them tackle challenges in developing, deploying, and scaling their analytics to reach its potential. Some topics he's enthusiastic about include MLOps, DataOps, GenerativeAI, Product thinking, and building cross-functional teams to deliver user-first products. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links QuantumBlack, AI by McKinsey: https://www.mckinsey.com/capabilities/quantumblack/how-we-help-clients --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Stephen on LinkedIn: https://www.linkedin.com/in/stephen-batifol/ Connect with Mara on LinkedIn: https://www.linkedin.com/in/mara-pometti Connect with Mohamed on LinkedIn: https://www.linkedin.com/in/mabusaid/…

MLOps.community

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Visualize - Bringing Structure to Unstructured Data // Markus Stoll // #258 50:38

45 weeks پیش50:38

50:38

Markus Stoll is the Co-Founder of Renumics and the developer behind the open-source interactive ML dataset exploration tool, Spotlight. He shares insights on: AI in Engineering and Manufacturing Interactive ML Data Visualization ML Data Exploration Follow Markus for hands-on articles about leveraging ML while keeping a strong focus on data. Visualize - Bringing Structure to Unstructured Data // MLOps Podcast #258 with Markus Stoll, CTO of Renumics. A huge thank you to SAS for their generous support! // Abstract This talk is about how data visualization and embeddings can support you in understanding your machine-learning data. We explore methods to structure and visualize unstructured data like text, images, and audio for applications ranging from classification and detection to Retrieval-Augmented Generation. By using tools and techniques like UMAP to reduce data dimensions and visualization tools like Renumics Spotlight, we aim to make data analysis for ML easier. Whether you're dealing with interpretable features, metadata, or embeddings, we'll show you how to use them all together to uncover hidden patterns in multimodal data, evaluate the model performance for data subgroups, and find failure modes of your ML models. // Bio Markus Stoll began his career in the industry at Siemens Healthineers, developing software for the Heavy Ion Therapy Center in Heidelberg. He learned about software quality while developing a treatment machine weighing over 600 tons. He earned a Ph.D., focusing on combining biomechanical models with statistical models, through which he learned how challenging it is to bridge the gap between research and practical application in the healthcare domain. Since co-founding Renumics, he has been active in the field of AI for Engineering, e.g., AI for Computer Aided Engineering (CAE), implementing projects, contributing to their open-source library for data exploration for ML datasets (Renumics Spotlight) and writing articles about data visualization. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://renumics.com/ MLSecOps Community: https://community.mlsecops.com/ Blogs: https://towardsdatascience.com/visualize-your-rag-data-evaluate-your-retrieval-augmented-generation-system-with-ragas-fc2486308557 : https://medium.com/itnext/how-to-explore-and-visualize-ml-data-for-object-detection-in-images-88e074f46361 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Markus on LinkedIn: https://www.linkedin.com/in/markus-stoll-b39a42138/…

MLOps.community

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AI Testing Highlights // Special MLOps Podcast Episode 9:54

46 weeks پیش9:54

9:54

MLOps for GenAI Applications // Special MLOps Podcast episode with Demetrios Brinkmann, Chief Happiness Engineer at MLOps Community. // Abstract Demetrios explores common themes in ML model testing with insights from Erica Greene (Yahoo News), Matar Haller (ActiveFence), Mohamed Elgendy (Kolena), and Catherine Nelson (Freelance Data Scientist). They discuss tiered test cases, functional testing for hate speech, differences between AI and traditional software testing, and the complexities of evaluating LLMs. Demetrios wraps up by inviting feedback and promoting an upcoming virtual conference on data engineering for AI and ML. // Bio At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps Community Podcasts. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Balancing Speed and Safety // Panel // AIQCON - https://youtu.be/c81puRgu3Kw AI For Good - Detecting Harmful Content at Scale // Matar Haller // MLOps Podcast #246 - https://youtu.be/wLKlZ6yHg1k What is AI Quality? // Mohamed Elgendy // MLOps Podcast #229 - https://youtu.be/-Jdmq4DiOew All Data Scientists Should Learn Software Engineering Principles // Catherine Nelson // Podcast #245 - https://youtu.be/yP6Eyny7p20 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Timestamps: [00:00] Exploring common themes in MLOps community [00:49] Common patterns about model output and testing [01:34] Tiered test case strategy [03:05] Functional testing for models [05:24] Testing coverage and quality [07:47] Evaluating LLMs challenges [08:35] Please like, share, leave a review, and subscribe to our MLOps channels!…

MLOps.community

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MLSecOps is Fundamental to Robust AISPM // Sean Morgan // #257 42:35

46 weeks پیش42:35

42:35

Sean Morgan is an active open-source contributor and maintainer and is the special interest group lead for TensorFlow Addons. Learn more about the platform for end-to-end AI Security at https://protectai.com/ . MLSecOps is Fundamental to Robust AI Security Posture Management (AISPM) // MLOps Podcast #257 with Sean Morgan, Chief Architect at Protect AI. // Abstract MLSecOps, which is the practice of integrating security practices into the AIML lifecycle (think infusing MLOps with DevSecOps practices), is a critical part of any team’s AI Security Posture Management. In this talk, we’ll discuss how to threat model realistic AIML security risks, how you can measure your organization’s AI Security Posture, and most importantly how you can improve that security posture through the use of MLSecOps. // Bio Sean Morgan is the Chief Architect at Protect AI. In prior roles he's led production AIML deployments in the semiconductor industry, evaluated adversarial machine learning defenses for DARPA research programs, and most recently scaled customers on interactive machine learning solutions at AWS. In his free time, Sean is an active open-source contributor and maintainer, and is the special interest group lead for TensorFlow Addons. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Sean's GitHub: https://github.com/seanpmorgan MLSecOps Community: https://community.mlsecops.com/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Sean on LinkedIn: https://www.linkedin.com/in/seanmorgan/ Timestamps: [00:00] Sean's preferred coffee [00:10] Takeaways [01:39] Register for the Data Engineering for AI/ML Conference now! [02:21] KubeCon Paris: Emphasis on security and AI [05:00] Concern about malicious data during training process [09:29] Model builders, security, pulling foundational models, nuances [12:13] Hugging Face research on security issues [15:00] Inference servers exposed; potential for attack [19:45] Balancing ML and security processes for ease [23:23] Model artifact security in enterprise machine learning [25:04] Scanning models and datasets for vulnerabilities [29:23] Ray's user interface vulnerabilities lead to attacks [32:07] ML Flow vulnerabilities present significant server risks [36:04] Data ops essential for machine learning security [37:32] Prioritized security in model and data deployment [40:46] Automated scanning tool for improved antivirus protection [42:00] Wrap up…

MLOps.community

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MLOps for GenAI Applications // Harcharan Kabbay // #256 1:07:18

46 weeks پیش1:07:18

1:07:18

Harcharan Kabbay is a Data Scientist & AI/ML Engineer with Expertise in MLOps, Kubernetes, and DevOps, Driving End-to-End Automation and Transforming Data into Actionable Insights. MLOps for GenAI Applications // MLOps Podcast #256 with Harcharan Kabbay, Lead Machine Learning Engineer at World Wide Technology. // Abstract The discussion begins with a brief overview of the Retrieval-Augmented Generation (RAG) framework, highlighting its significance in enhancing AI capabilities by combining retrieval mechanisms with generative models. The podcast further explores the integration of MLOps, focusing on best practices for embedding the RAG framework into a CI/CD pipeline. This includes ensuring robust monitoring, effective version control, and automated deployment processes that maintain the agility and efficiency of AI applications. A significant portion of the conversation is dedicated to the importance of automation in platform provisioning, emphasizing tools like Terraform. The discussion extends to application design, covering essential elements such as key vaults, configurations, and strategies for seamless promotion across different environments (development, testing, and production). We'll also address how to enhance the security posture of applications through network firewalls, key rotation, and other measures. Let's talk about the power of Kubernetes and related tools to aid a good application design. The podcast highlights the principles of good application design, including proper observability and eliminating single points of failure. I would share strategies to reduce development time by creating templates for GitHub repositories by application types to be re-used, also templates for pull requests, thereby minimizing human errors and streamlining the development process. // Bio Harcharan is an AI and machine learning expert with a robust background in Kubernetes, DevOps, and automation. He specializes in MLOps, facilitating the adoption of industry best practices and platform provisioning automation. With extensive experience in developing and optimizing ML and data engineering pipelines, Harcharan excels at integrating RAG-based applications into production environments. His expertise in building scalable, automated AI systems has empowered the organization to enhance decision-making and problem-solving capabilities through advanced machine-learning techniques. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Harcharan's Medium - https://medium.com/@harcharan-kabbay Data Engineering for AI/ML Conference: https://home.mlops.community/home/events/dataengforai --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Harcharan on LinkedIn: https://www.linkedin.com/in/harcharankabbay/locale=en_US…

MLOps.community

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BigQuery Feature Store // Nicolas Mauti // #255 50:38

47 weeks پیش50:38

50:38

Nicolas Mauti is an MLOps Engineer from Lyon (France), Working at Malt . BigQuery Feature Store // MLOps Podcast #255 with Nicolas Mauti, Lead MLOps at Malt. // Abstract Need a feature store for your AI/ML applications but overwhelmed by the multitude of options? Think again. In this talk, Nicolas shares how they solved this issue at Malt by leveraging the tools they already had in place. From ingestion to training, Nicolas provides insights on how to transform BigQuery into an effective feature management system. We cover how Nicolas' team designed their feature tables and addressed challenges such as monitoring, alerting, data quality, point-in-time lookups, and backfilling. If you’re looking for a simpler way to manage your features without the overhead of additional software, this talk is for you. Discover how BigQuery can handle it all! // Bio Nicolas Mauti is the go-to guy for all things related to MLOps at Malt. With a knack for turning complex problems into streamlined solutions and over a decade of experience in code, data, and ops, he is a driving force in developing and deploying machine learning models that actually work in production. When he's not busy optimizing AI workflows, you can find him sharing his knowledge at the university. Whether it's cracking a tough data challenge or cracking a joke, Nicolas knows how to keep things interesting. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Nicolas' Medium - https://medium.com/@nmauti Data Engineering for AI/ML Conference: https://home.mlops.community/home/events/dataengforai --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Nicolas on LinkedIn: https://www.linkedin.com/in/nicolasmauti/?locale=en_US Timestamps: [00:00] Nicolas' preferred beverage [00:35] Takeaways [02:25] Please like, share, leave a review, and subscribe to our MLOps channels! [02:57] BigQuery end goal [05:00] BigQuery pain points [10:14] BigQuery vs Feature Stores [12:54] Freelancing Rate Matching issues [16:43] Post-implementation pain points [19:39] Feature Request Process [20:45] Feature Naming Consistency [23:42] Feature Usage Analysis [26:59] Anomaly detection in data [28:25] Continuous Model Retraining Process [30:26] Model misbehavior detection [33:01] Handling model latency issues [36:28] Accuracy vs The Business [38:59] BigQuery cist-benefit analysis [42:06] Feature stores cost savings [44:09] When not to use BigQuery [46:20] Real-time vs Batch Processing [49:11] Register for the Data Engineering for AI/ML Conference now! [50:14] Wrap up…

MLOps.community

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Design and Development Principles for LLMOps // Andy McMahon // #254 1:10:17

47 weeks پیش1:10:17

1:10:17

Design and Development Principles for LLMOps // MLOps Podcast #254 with Andy McMahon, Director - Principal AI Engineer at Barclays Bank. A huge thank you to SAS for their generous support! // Abstract As we move from MLOps to LLMOps we need to double down on some fundamental software engineering practices, as well as augment and add to these with some new techniques. In this case, let's talk about this! // Bio Andy is a Principal AI Engineer, working in the new AI Center of Excellence at Barclays Bank. Previously he was Head of MLOps for NatWest Group, where he led their MLOps Centre of Excellence and helped build out their MLOps platform and processes across the bank. Andy is also the author of Machine Learning Engineering with Python, a hands-on technical book published by Packt. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Andy's book - https://packt.link/w3JKL Andy's Medium - https://medium.com/@andrewpmcmahon629 SAS: https://www.sas.com/en_us/home.html SAS® Decision Builder: https://www.sas.com/en_us/offers/23q4/microsoft-fabric.html Data Engineering for AI/ML Conference: https://home.mlops.community/home/events/dataengforai Harnessing MLOps in Finance // Michelle Marie Conway // MLOps Podcast Coffee #174: https://youtu.be/nIEld_Q6L-0The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses book by Eric Ries: https://www.amazon.co.jp/-/en/Eric-Ries/dp/0307887898 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Andy on LinkedIn: https://www.linkedin.com/in/andrew-p-mcmahon/ Timestamps: [00:00] Andy's preferred coffee [00:09] Takeaways [02:04] Andy's book as an Oxford curriculum [06:13] Register for the Data Engineering for AI/ML Conference now! [07:04] The Life Cycle of AI Executives Course [09:55] MLOps as a term [11:53] Tooling vs Process Culture [15:01] Open source benefits [17:15] End goal flexibility [20:06] Hybrid Cloud Strategy Overview [21:11] ROI for tool upgrades [25:41] Long-term projects comparison [29:02 - 30:48] SAS Ad [30:49] AI and ML Integration [35:40] Hybrid AI Integration Insights [42:18] Tech trends vs Practicality [44:39] Gen AI Tooling Debate [51:57] Vanity metrics overview [55:22] Tech business alignment strategy [58:45] Aligning teams for ROI [1:01:35] Communication mission effectively [1:03:45] Enablement metrics [1:06:38] Prioritizing use cases [1:09:47] Wrap up…

MLOps.community

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Data Quality = Quality AI // AIQCON Panel 27:14

48 weeks پیش27:14

27:14

// Abstract Data is the foundation of AI. To ensure AI performs as expected, high-quality data is essential. In this panel discussion, Chad, Maria, Joe, and Pushkar hosted by Sam Partee will explore strategies for obtaining and maintaining high-quality data, as well as common pitfalls to avoid when using data for AI models. // Panelists - Samuel Partee: Principal Applied AI Engineer @ Redis - Chad Sanderson: CEO & Co-Founder @ Gable - Joe Reis: CEO/Co-Founder @ Ternary Data - Maria Zhang: CEO Cofounder @ Proactive AI Lab Inc - Pushkar Garg: Staff Machine Learning Engineer @ Clari Inc.…

MLOps.community

1
The Variational Book // Yuri Plotkin // #253 55:35

48 weeks پیش55:35

55:35

Yuri Plotkin is a Biomedical Engineer and Machine Learning Scientist and the author of The Variational Book . The Variational Book // MLOps Podcast #253 with Yuri Plotkin, an ML Scientist. // Abstract Curiosity has been the underlying thread in Yuri's life and interests. With the explosion of Generative AI, Yuri was fascinated by the topic and decided he needed to learn more. Yuri pursued learning by reading, deriving, and understanding seminal papers within the last generation. The endeavors culminated in the writing of a book on the topic, The Variational Book, which Yuri expects to release shortly in the coming months. A bit of detail about the topics he covers can be found here: www.thevariationalbook.com. // Bio Evolved from biomedical engineer to wet-lab scientist, and more recently transitioned Yuri's career to computer science with the last 10+ years developing projects at the intersection of medicine, life sciences and machine learning. Yuri's educational background is in Biomedical Engineering, at Columbia University (M.S.) and University of California, San Diego (B.S.). Current interests include generative AI, diffusion models, and LLMs. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://plotkiny.github.io/ The Variational Book: www.thevariationalbook.com SAS: https://www.sas.com/en_us/home.html SAS® Decision Builder: https://www.sas.com/en_us/offers/23q4/microsoft-fabric.html Data Engineering for AI/ML Conference: https://home.mlops.community/home/events/dataengforai --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Yuri on LinkedIn: http://www.linkedin.com/in/yuri-plotkin/…

MLOps.community

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Vision and Strategies for Attracting & Driving AI Talents in High Growth // Panel // AIQCON 30:33

49 weeks پیش30:33

30:33

// Abstract Attracting and retaining top AI talent is essential for staying competitive. This panel will explore crafting and communicating a compelling vision that aligns with the organization's evolving needs, inspiring potential hires and motivating current employees. The discussion will offer actionable strategies for sourcing top talent, adapting to changing needs, and maintaining company alignment. Attendees will learn best practices for attracting AI professionals, creating an attractive employer brand, and enhancing talent acquisition and retention strategies. Lastly, the panel will cover structuring and organizing the AI team as it grows to ensure alignment with business goals. This includes optimal team configurations, leadership roles, and processes that support collaboration and innovation, enabling sustained growth and success. // PANELISTS Ashley Antonides : Associate Research Director, AI/ML @ Two Six Technologies Olga Beregovaya : VP, AI @ Smartling Shailvi Wakhlu : Founder @ Shailvi Ventures LLC A big thank you to our Premium Sponsors Google Cloud & Databricks for their generous support!…

MLOps.community

1
Red Teaming LLMs // Ron Heichman // #252 1:09:52

49 weeks پیش1:09:52

1:09:52

Ron Heichmn is an AI researcher specializing in generative AI, AI alignment, and prompt engineering. At SentinelOn e , Ron actively monitors emerging research to identify and address potential vulnerabilities in our AI systems, focusing on unsupervised and scalable evaluations to ensure robustness and reliability. Harnessing AI APIs for Safer, Accurate, & Reliable Applications // MLOps Podcast #252 with Ron Heichman, Machine Learning Engineer at SentinelOne. // Abstract Integrating AI APIs effectively is pivotal for building applications that leverage LLMs, especially given the inherent issues with accuracy, reliability, and safety that LLMs often exhibit. I aim to share practical strategies and experiences for using AI APIs in production settings, detailing how to adapt these APIs to specific use cases, mitigate potential risks, and enhance performance. The focus will be testing, measuring, and improving quality for RAG or knowledge workers utilizing AI APIs. // Bio Ron Heichman is an AI researcher and engineer dedicated to advancing the field through his work on prompt injection at Preamble, where he helped uncover critical vulnerabilities in AI systems. Currently at SentinelOne, he specializes in generative AI, AI alignment, and the benchmarking and measurement of AI system performance, focusing on Retrieval-Augmented Generation (RAG) and AI guardrails. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.sentinelone.com/ All the Hard Stuff with LLMs in Product Development // Phillip Carter // MLOps Podcast #170: https://www.youtube.com/watch?v=DZgXln3v85s&ab_channel=MLOps.community --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Ron on LinkedIn: https://www.linkedin.com/in/heichmanron/…

MLOps.community

1
Balancing Speed and Safety // Panel // AIQCON 35:39

50 weeks پیش35:39

35:39

This is a panel taken from the recent AI quality Conference presented by the MLOps Community and Kolena // Abstract The need for moving to production quickly is paramount in staying out of perpetual POC territory. AI is moving fast. Shipping features fast to stay ahead of the competition is commonplace. Quick iterations are viewed as strength in the startup ecosystem, especially when taking on a deeply entrenched competitor. Each week a new method to improve your AI system becomes popular or a SOTA foundation model is released. How do we balance the need for speed vs the responsibility of safety? Having the confidence to ship a cutting-edge model or AI architecture and knowing it will perform as tasked. What are the risks and safety metrics that others are using when they deploy their AI systems. How can you correctly identify when risks are too large? // Panelists - Remy Thellier: Head of Growth & Strategic Partnerships @ Vectice - Erica Greene: Director of Engineering, Machine Learning @ Yahoo - Shreya Rajpal: Creator @ Guardrails AI A big thank you to our Premium Sponsors Google Cloud & Databricks for their generous support!…

MLOps.community

1
Reliable LLM Products, Fueled by Feedback // Chinar Movsisyan // #251 49:16

50 weeks پیش49:16

49:16

Chinar Movsisyan is the co-founder and CEO of Feedback Intelligence (formerly Manot) , an MLOps startup based in San Francisco. She has been in the AI field for more than 7 years from research labs to venture-backed startups. Reliable LLM Products, Fueled by Feedback // MLOps Podcast #250 with Chinar Movsisyan, CEO of Feedback Intelligence. // Abstract We live in a world driven by large language models (LLMs) and generative AI, but ensuring they are ready for real-world deployment is crucial. Despite the availability of numerous evaluation tools, many LLM products still struggle to make it to production. We propose a new perspective on how LLM products should be measured, evaluated, and improved. A product is only as good as the user's experience and expectations, and we aim to enhance LLM products to meet these standards reliably. Our approach creates a new category that automates the need for separate evaluation, observability, monitoring, and experimentation tools. By starting with the user experience and working backward to the model, we provide a comprehensive view of how the product is actually used, rather than how it is intended to be used. This user-centric aka feedback-centric approach is the key to every successful product. // Bio Chinar Movsisyan is the founder and CEO of Feedback Intelligence, an MLOps company based in San Francisco that enables enterprises to make sure that LLM-based products are reliable and that the output is aligned with end-user expectations. With over eight years of experience in deep learning, spanning from research labs to venture-backed startups, Chinar has led AI projects in mission-critical applications such as healthcare, drones, and satellites. Her primary research interests include artificial intelligence, generative AI, machine learning, deep learning, and computer vision. At Feedback Intelligence, Chinar and her team address a crucial challenge in LLM development by automatically converting user feedback into actionable insights, enabling AI teams to analyze root causes, prioritize issues, and accelerate product optimization. This approach is particularly valuable in highly regulated industries, helping enterprises to reduce time-to-market and time-to-resolution while ensuring robust LLM products. Feedback Intelligence, which participated in the Berkeley SkyDeck accelerator program, is currently expanding its business across various verticals. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.manot.ai/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Chinar on LinkedIn: https://www.linkedin.com/in/nik-suresh/ Timestamps: [00:00] Chinar's preferred coffee [00:20] Takeaways [02:25] Please like, share, leave a review, and subscribe to our MLOps channels! [03:23] Object Detection on Drones [06:10] Street Surveillance Detection Use Case [08:00] Optimizing Vision Models [09:50] Data Engineering for AI/ML Conference Ad [10:42] Plastic surgery project [12:33] Diffusion models getting popular [13:57] AI challenges in highly regulated industries [17:48] Product metrics evaluation insights [20:55] Chatbot effectiveness metrics [23:15] Interpreting user signals [24:45] Metadata tracking in LLM [27:41] Agentic workflow [28:53] Effective data analysis strategies [30:41] Identifying key metrics [33:59] AI metrics role shift [37:20] Tooling for non-engineers [42:12] Balancing engineering and evaluation [44:39] Bridging SME engineering gap [46:41] Expand expertise potential [47:40] What's with flamingos [48:04] Wrap up…

MLOps.community

1
A Blueprint for Scalable & Reliable Enterprise AI/ML Systems // Panel // AIQCON 35:38

51 weeks پیش35:38

35:38

This is a Panel taken from the recent AI Quality Conference presented by the MLOps COmmunity and Kolena // Abstract Enterprise AI leaders continue to explore the best productivity solutions that solve business problems, mitigate risks, and increase efficiency. Building reliable and secure AI/ML systems requires following industry standards, an operating framework, and best practices that can accelerate and streamline the scalable architecture that can produce expected business outcomes. This session, featuring veteran practitioners, focuses on building scalable, reliable, and quality AI and ML systems for the enterprises. // Panelists - Hira Dangol: VP, AI/ML and Automation @ Bank of America - Rama Akkiraju: VP, Enterprise AI/ML @ NVIDIA - Nitin Aggarwal: Head of AI Services @ Google - Steven Eliuk: VP, AI and Governance @ IBM A big thank you to our Premium Sponsors Google Cloud & Databricks for their generous support! Timestamps: 00:00 Panelists discuss vision and strategy in AI 05:18 Steven Eliuk, IBM expertise in data services 07:30 AI as means to improve business metrics 11:10 Key metrics in production systems: efficiency and revenue 13:50 Consistency in data standards aids data integration 17:47 Generative AI presents new data classification risks 22:47 Evaluating implications, monitoring, and validating use cases 26:41 Evaluating natural language answers for efficient production 29:10 Monitoring AI models for performance and ethics 31:14 AI metrics and user responsibility for future models 34:56 Access to data is improving, promising progress…

MLOps.community

1
AI Operations Without Fundamental Engineering Discipline // Nikhil Suresh // #250 49:28

51 weeks پیش49:28

49:28

Nik Suresh wrote an evisceration of the current AI hype boom called "I Will F**king Piledrive You If You Mention AI Again." AI Operations Without Fundamental Engineering Discipline // MLOps Podcast #250 with Nikhil Suresh, Director @ Hermit Tech. // Abstract Nik is on the podcast because of an anti-AI hype piece, so a reasonable thing to discuss is going to be what most companies are getting wrong when non-technical management wants to immediately roll out ML initiatives, but are unwilling to bring technical naysayers on board who will set them up for success. // Bio Nik is the author of ludic.mataroa.blog, who wrote "I Will [REDACTED] Piledriver You If You Mention AI Again", and mostly works in the data engineering and data science spaces. Nik's writing and company both focus on bringing more care to work, pushing back against the industry's worst excesses both technically and spiritually, and getting fundamentals right. Nik also has a reasonably strong background in psychology. His data science training was of the pre-LLM variety, circa. 2018, when there was a lot of hype but it wasn't this ridiculous. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://ludic.mataroa.blog/ Nik's blog: https://ludic.mataroa.blog/blog/i-will-fucking-piledrive-you-if-you-mention-ai-again/ Harnessing MLOps in Finance // Michelle Marie Conway // MLOps Podcast Coffee #174: https://youtu.be/nIEld_Q6L-0 Fundamentals of Data Engineering: Plan and Build Robust Data Systems AudiobookBy: Joe Reis, Matt Housley: https://audiobookstore.com/audiobooks/fundamentals-of-data-engineering.aspx Bullshit Jobs A Theory Hardcover by David Graeber : https://www.amazon.co.jp/-/en/David-Graeber/dp/0241263883 Does a Frog have Scorpion Nature podcast: https://open.spotify.com/show/57i8sYVqxG4i3NvBniLfhv --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Nik on LinkedIn: https://www.linkedin.com/in/nik-suresh/ Timestamps: [00:00] Nik's preferred coffee [00:30] Takeaways [01:40] Please like, share, leave a review, and subscribe to our MLOps channels! [01:56] AI hype and humor [07:21] Defining project success [08:57] Effective data utilization [12:18] AI Hype vs Data Engineering [14:44] AI implementation challenges [17:44 - 18:35] Data Engineering for AI and ML Virtual Conference Ad [18:35] Managing AI Expectations [22:08] AI expectations vs reality [26:00] Balancing Engineering and AI [31:54] Highlighting engineer success [35:25] The real challenges [36:30] Embracing work challenges [37:21] Dealing with podcast disappointments [40:50] Creating content for visibility [43:02] Exploring niche interests [44:14] Relationship building [47:15] Strategic approach to success [48:36] Wrap up…

MLOps.community

1
AI in Healthcare // Eric Landry // #249 51:05

52 weeks پیش51:05

51:05

Eric Landry is a seasoned AI and Machine Learning leader with extensive expertise in software engineering and practical applications in NLP, document classification, and conversational AI. With technical proficiency in Java, Python, and key ML tools, he leads the Expedia Machine Learning Engineering Guild and has spoken at major conferences like Applied Intelligence 2023 and KDD 2020. AI in Healthcare // MLOps Podcast #249 with Eric Landry, CTO/CAIO @ Zeteo Health. // Abstract Eric Landry discusses the integration of AI in healthcare, highlighting use cases like patient engagement through chatbots and managing medical data. He addresses benchmarking and limiting hallucinations in LLMs, emphasizing privacy concerns and data localization. Landry maintains a hands-on approach to developing AI solutions and navigating the complexities of healthcare innovation. Despite necessary constraints, he underscores the potential for AI to proactively engage patients and improve health outcomes. // Bio Eric Landry is a technology veteran with 25+ years of experience in the healthcare, travel, and computer industries, specializing in machine learning engineering and AI-based solutions. Holding a Masters in SWE (NLP thesis topic) from the University of Texas at Austin, 2005. He has showcased his expertise and leadership in the field with three US patents, published articles on machine learning engineering, and speaking engagements at the 2023 Applied Intelligence Live, 2020 KDD conference, Data Science Salon 2024, and former leader of Expedia’s MLE guild. Formerly, Eric was the director of AI Engineering and Conversation Platform at Babylon Health and Expedia. Currently CTO/CAIO at Zeteo Health. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.zeteo.health/ Building Threat Detection Systems: An MLE's Perspective // Jeremy Jordan // MLOps Podcast #134: https://youtu.be/13nOmMJuiAo --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Eric on LinkedIn: https://www.linkedin.com/in/jeric-landry/ Timestamps: [00:00] Eric's preferred coffee [00:16] Takeaways [01:16] Please like, share, leave a review, and subscribe to our MLOps channels! [01:32] ML and AI in 2005 [04:43] Last job at Babylon Health [10:57] Data access solutions [14:35] Prioritize AI ML Team Success [16:39] Eric's current work [20:36] Engage in holistic help [22:13] High-stakes chatbots [27:30] Navigating Communication Across Diverse Communities [31:49] When Bots Go Wrong [34:15] Health care challenges ahead [36:05] Behavioral health tech challenges [39:45] Stress from Apps Notifications [41:11] Combining different guardrails tools [47:16] Navigating Privacy AI [50:12] Wrap up…

MLOps.community

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Evaluating the Effectiveness of Large Language Models: Challenges and Insights // Aniket Singh // #248 35:40

1 year پیش35:40

35:40

Aniket Kumar Singh is a Vision Systems Engineer at Ultium Cells, skilled in Machine Learning and Deep Learning. I'm also engaged in AI research, focusing on Large Language Models (LLMs). Evaluating the Effectiveness of Large Language Models: Challenges and Insights // MLOps Podcast #248 with Aniket Kumar Singh, CTO @ MyEvaluationPal | ML Engineer @ Ultium Cells. // Abstract Dive into the world of Large Language Models (LLMs) like GPT-4. Why is it crucial to evaluate these models, how we measure their performance, and the common hurdles we face? Drawing from Aniket's research, he shares insights on the importance of prompt engineering and model selection. Aniket also discusses real-world applications in healthcare, economics, and education, and highlights future directions for improving LLMs. // Bio Aniket is a Vision Systems Engineer at Ultium Cells, skilled in Machine Learning and Deep Learning. I'm also engaged in AI research, focusing on Large Language Models (LLMs). // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.aniketsingh.me Aniket's AI Research for Good blog that I plan to utilize to share any new research that would focus on the good: www.airesearchforgood.org Aniket's papers: https://scholar.google.com/citations?user=XHxdWUMAAAAJ&hl=en --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Aniket on LinkedIn: https://www.linkedin.com/in/singh-k-aniket/ Timestamps: [00:00] Aniket's preferred coffee [00:14] Takeaways [01:29] Aniket's job and hobby [03:06] Evaluating LLMs: Systems-Level Perspective [05:55] Rule-based system [08:32] Evaluation Focus: Model Capabilities [13:04] LLM Confidence [13:56] Problems with LLM Ratings [17:17] Understanding AI Confidence Trends [18:28] Aniket's papers [20:40] Testing AI Awareness [24:36] Agent Architectures Overview [27:05] Leveraging LLMs for tasks [29:53] Closed systems in Decision-Making [31:28] Navigating model Agnosticism [33:47] Robust Pipeline vs Robust Prompt [34:40] Wrap up…

MLOps.community

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Extending AI: From Industry to Innovation // Sophia Rowland & David Weik // #247 1:01:36

1 year پیش1:01:36

1:01:36

Sophia Rowland is a Senior Product Manager focusing on ModelOps and MLOps at SAS . In her previous role as a data scientist, Sophia worked with dozens of organizations to solve a variety of problems using analytics. David Weik has a passion for data and creating integrated customer-centric solutions. Thinking data and people first to create value-added solutions. Extending AI: From Industry to Innovation // MLOps Podcast #247 with Sophia Rowland, Senior Product Manager and David Weik, Senior Solutions Architect of SAS. Huge thank you to SAS for sponsoring this episode. SAS - http://www.sas.com/ // Abstract Organizations worldwide invest hundreds of billions into AI, but they do not see a return on their investments until they are able to leverage their analytical assets and models to make better decisions. At SAS, we focus on optimizing every step of the Data and AI lifecycle to get high-performing models into a form and location where they drive analytically driven decisions. Join experts from SAS as they share learnings and best practices from implementing MLOps and LLMOPs at organizations across industries, around the globe, and using various types of models and deployments, from IoT CV problems to composite flows that feature LLMs. // Bio Sophia Rowland Sophia Rowland is a Senior Product Manager focusing on ModelOps and MLOps at SAS. In her previous role as a data scientist, Sophia worked with dozens of organizations to solve a variety of problems using analytics. As an active speaker and writer, Sophia has spoken at events like All Things Open, SAS Explore, and SAS Innovate as well as written dozens of blogs and articles. As a staunch North Carolinian, Sophia holds degrees from both UNC-Chapel Hill and Duke including bachelor’s degrees in computer science and psychology and a Master of Science in Quantitative Management: Business Analytics from the Fuqua School of Business. Outside of work, Sophia enjoys reading an eclectic assortment of books, hiking throughout North Carolina, and trying to stay upright while ice skating. David Weik David joined SAS in 2020 as a solutions architect. He helps customers to define and implement data-driven solutions. Previously, David was a SAS administrator/developer at a German insurance company working with the integration capabilities of SAS, Robotic Process Automation, and more. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links http://www.sas.com/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Sophia on LinkedIn: https://www.linkedin.com/in/sophia-rowland/ Connect with David on LinkedIn: https://www.linkedin.com/in/david-weik/ Timestamps: [00:00] Sophia & David's preferred coffee [00:19] Takeaways [02:11] Please like, share, leave a review, and subscribe to our MLOps channels! [02:55] Hands on MLOps and AI [05:14] Next-Gen MLOps Challenges [07:24] Data scientists adopting software [11:48] Taking a different approach [13:43] Zombie Model Management [16:36] Optimizing ML Revenue Allocation [18:39] Other use cases - Lockout - Tagout procedure [21:43] Vision Model Integration Challenges [26:16] Costly errors in predictive maintenance [27:25] Integration of Gen AI [34:32] Governance challenges in AI [38:00] Governance in Gen AI vs Governance with Traditional ML [41:53] Evaluation challenges in industries [46:49] Interface frustration with Chatbots [51:25] Implementing AI Agent's success [54:18] Usability challenges in interfaces [57:03] Themes in High-Performing AI Teams [1:00:51] Wrap up…

MLOps.community

1
Detecting Harmful Content at Scale // Matar Haller // #246 51:27

1 year پیش51:27

51:27

Matar Haller is the VP of Data & AI at ActiveFence , where her teams own the end-to-end automated detection of harmful content at scale, regardless of the abuse area or media type. The work they do here is engaging, impactful, and tough, and Matar is grateful for the people she gets to do it with. AI For Good - Detecting Harmful Content at Scale // MLOps Podcast #246 with Matar Haller, VP of Data & AI at ActiveFence. // Abstract One of the biggest challenges facing online platforms today is detecting harmful content and malicious behavior. Platform abuse poses brand and legal risks, harms the user experience, and often represents a blurred line between online and offline harm. So how can online platforms tackle abuse in a world where bad actors are continuously changing their tactics and developing new ways to avoid detection? // Bio Matar Haller leads the Data & AI Group at ActiveFence, where her teams are responsible for the data, algorithms, and infrastructure that fuel ActiveFence’s ability to ingest, detect, and analyze harmful activity and malicious content at scale in an ever-changing, complex online landscape. Matar holds a Ph.D. in Neuroscience from the University of California at Berkeley, where she recorded and analyzed signals from electrodes surgically implanted in human brains. Matar is passionate about expanding leadership opportunities for women in STEM fields and has three children who surprise and inspire her every day. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links activefence.com https://www.youtube.com/@ActiveFence --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Matar on LinkedIn: https://www.linkedin.com/company/11682234/admin/feed/posts/ Timestamps: [00:00] Matar's preferred coffee [00:13] Takeaways [01:39] The talk that stood out [06:15] Online hate speech challenges [08:13] Evaluate harmful media API [09:58] Content moderation: AI models [11:36] Optimizing speed and accuracy [13:36] Cultural reference AI training [15:55] Functional Tests [20:05] Continuous adaptation of AI [26:43] AI detection concerns [29:12] Fine-Tuned vs Off-the-Shelf [32:04] Monitoring Transformer Model Hallucinations [34:08] Auditing process ensures accuracy [38:38] Testing strategies for ML [40:05] Modeling hate speech deployment [42:19] Improving production code quality [43:52] Finding balance in Moderation [47:23] Model's expertise: Cultural Sensitivity [50:26] Wrap up…

MLOps.community

1
All Data Scientists Should Learn Software Engineering Principles // Catherine Nelson // #245 52:54

1 year پیش52:54

52:54

Catherine Nelson is a freelance data scientist and writer. She is currently working on the forthcoming O’Reilly book " Software Engineering for Data Scientists ”. Why All Data Scientists Should Learn Software Engineering Principles // MLOps podcast #245 with Catherine Nelson, a freelance Data Scientist. A big thank you to LatticeFlow AI for sponsoring this episode! LatticeFlow AI - https://latticeflow.ai/ // Abstract Data scientists have a reputation for writing bad code. This quote from Reddit sums up how many people feel: “It's honestly unbelievable and frustrating how many Data Scientists suck at writing good code.” But as data science projects grow, and because the job now often includes deploying ML models, it's increasingly important for DSs to learn fundamental SWE principles such as keeping your code modular, making sure your code is readable by other people and so on. The exploratory nature of DS projects means that you can't be sure where you will end up at the start of a project, but there's still a lot you can do to standardize the code you write. // Bio Catherine Nelson is the author of "Software Engineering for Data Scientists", a guide for data scientists who want to level up their coding skills, published by O'Reilly in May 2024. She is currently consulting for GenAI startups and providing mentorship and career coaching to data scientists. Previously, she was a Principal Data Scientist at SAP Concur. She has extensive experience deploying NLP models to production and evaluating ML systems, and she is also co-author of the book "Building Machine Learning Pipelines", published by O'Reilly in 2020. In her previous career as a geophysicist, she studied ancient volcanoes and explored for oil in Greenland. Catherine has a PhD in geophysics from Durham University and a Masters of Earth Sciences from Oxford University. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Software Engineering for Data Scientists book by Catherine Nelson: https://learning.oreilly.com/library/view/software-engineering-for/9781098136192/ https://www.amazon.com/Software-Engineering-Data-Scientists-Notebooks/dp/1098136209 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Catherine on LinkedIn: https://www.linkedin.com/in/catherinenelson1/ Timestamps: [00:00] Catherine's preferred coffee [00:15] Takeaways [02:38] Meeting magic: Embracing serenity [06:23] The Software Engineering for Data Scientists book [10:41] Exploring ideas rapidly [12:52] Bridging Data Science gaps [16:17] Data poisoning concerns [18:26] Transitioning from a data scientist to a machine learning engineer [21:53] Rapid Prototyping vs Thorough Development [23:45] Data scientists take ownership [25:53] Data scientists' role balance [30:30] Understanding system design process [36:00] Data scientists and Kubernetes [41:33 - 43:03] LatticeFlow AI Ad [43:05] The Future of Data Science [45:09] Data scientists analyzing models [46:46] Tools gaps in prompt tracking [50:44] Learnings from writing the book…

MLOps.community

1
Meta GenAI Infra Blog Review // Special MLOps Podcast 38:53

1 year پیش38:53

38:53

Meta GenAI Infra Blog Review // Special MLOps Podcast episode by Demetrios. // Abstract Demetrios explores Meta's innovative infrastructure for large-scale AI operations, highlighting three blog posts on training large language models, maintaining AI capacity, and building Meta's GenAI infrastructure. The discussion reveals Meta's handling of hundreds of trillions of AI model executions daily, focusing on scalability, cost efficiency, and robust networking. Key elements include the Ops planner work orchestrator, safety protocols, and checkpointing challenges in AI training. Meta's efforts in hardware design, software solutions, and networking optimize GPU performance, with innovations like a custom Linux file system and advanced networking file systems like Hammerspace. The podcast also discusses advancements in PyTorch, network technologies like Roce and Nvidia's Quantum 2 Infiniband fabric, and Meta's commitment to open-source AGI. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Building Meta’s GenAI Infrastructure blog: https://engineering.fb.com/2024/03/12/data-center-engineering/building-metas-genai-infrastructure/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Timestamps: [00:00] Meta handles trillions of AI model executions [07:01] Meta creating AGI, ethical and sustainable [08:13] Concerns about energy use in training models [12:22] Network, hardware, and job optimization for reliability [17:21] Highlights of Arista and Nvidia hardware architecture [20:11] Meta's clusters optimized for efficient fabric [24:40] Varied steps, careful checkpointing in AI training [28:46] Meta is maintaining huge GPU clusters for AI [29:47] AI training is faster and more demanding [35:27] Ops planner orchestrates a million operations and reduces maintenance [37:15] Ops planner ensures safety and well-tested changes…

MLOps.community

1
AI Agents for Consumers // Shaun Wei // #244 57:26

1 year پیش57:26

57:26

Sean Wei , the CEO and co-founder of RealChar , shares his journey from working in the autonomous vehicle industry to creating an open-source voice assistant project called Realchar, which eventually evolved into Rivia , a voice AI assistant focused on managing personal phone calls. The Future of AI and Consumer Empowerment // MLOps podcast #244 with Shaun Wei, CEO & Co-Founder of RealChar. A big thank you to LatticeFlow for sponsoring this episode! LatticeFlow - https://latticeflow.ai/ // Abstract Explore the groundbreaking work RealChar is doing with its consumer application, Rivia. This discussion focuses on how Rivia leverages Generative AI and Traditional Machine Learning to handle mundane phone calls and customer service interactions, aiming to free up human time for more meaningful tasks. The product, currently in beta, embodies a forward-thinking approach to AI, where the technology offloads day-to-day burdens like scheduling appointments and making calls. // Bio Shaun Wei is a well-connected technology professional with a rich background in developing and analyzing artificial intelligence systems. In 2018, Shaun played a pivotal role in the advent and deployment of Google Duplex, a remarkable AI capable of handling natural conversations and performing tasks such as booking hair salon appointments and restaurant reservations via telephone. His involvement wasn't just limited to the developmental side; Shaun also uniquely positioned himself on the receiving end, gathering insights by interviewing users directly impacted by the technology. This dual perspective has enabled Shaun to grasp both the technical underpinnings and the human-centric applications of AI, making him a valuable asset in the tech industry. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links https://www.rivia.tech/ https://realchar.ai/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Shaun on LinkedIn: https://www.linkedin.com/in/shaunwei/ Timestamps: [00:00] Shaun's preferred coffee [00:28] Takeaways [03:30] Please like, share, leave a review, and subscribe to our MLOps channels! [03:57] AI in Production: Challenges & Insights [06:13] AI Scheduling and Assistance [08:00] Technical Challenges in AI [12:36 - 14:06] LatticeFlow Ad [14:09] Handling Challenges in AI [15:52] Learning driving and technical aspects [19:04] Self-Driving Cars: Multimodal Integration [23:41] Processing data with Transformers [26:46] Real-time phone data gathering [30:49] Real-time observability in AI [35:09] Time to first token [37:26] Preferred vs. Dynamic Model Selection [40:12] Event-driven architecture basics [42:06] Navigating challenges together [44:02] Challenges with Inconsistent Responses [45:40] Importance of product reliability [47:47] Training Data and Model Performance [50:02] Exploring AI in Customer Service [51:34] Navigating challenges in AI [53:15] Excited Launch Strategy Advice [57:10] Wrap up…

MLOps.community

1
ML and AI as Distinct Control Systems in Heavy Industrial Settings // Richard Howes // #243 56:30

1 year پیش56:30

56:30

Join us at our first in-person conference today all about AI Quality: https://www.aiqualityconference.com/ ML and AI as Distinct Control Systems in Heavy Industrial Settings // MLOps podcast #243 with Richard Howes, CTO of Metaformed. Richard Howes is a dedicated engineer who is passionate about control systems whether it be embedded systems, industrial automation, or AI/ML in a business application. Huge thank you to AWS for sponsoring this episode. AWS - https://aws.amazon.com/ // Abstract How can we balance the need for safety, reliability, and robustness with the extreme pace of technology advancement in heavy industry? The key to unlocking the full potential of data will be to have a mixture of experts both from an AI and human perspective to validate anything from a simple KPI to a Generative AI Assistant guiding operators throughout their day. The data generated by heavy industries like agriculture, oil & gas, forestry, real estate, civil infrastructure, and manufacturing is underutilized and struggles to keep up with the latest and greatest - and for good reason. They provide the shelter we live and work in, the food we eat, and the energy to propel society forward. Compared to the pace of AI innovation they move slowly, have extreme consequences for failure, and typically involve a significant workforce. During this discussion, we will outline the data ready to be utilized by ML, AI, and data products in general as well as some considerations for creating new data products for these heavy industries. To account for complexity and uniqueness throughout the organization it is critical to engage operational staff, ensure safety is considered from all angles, and build adaptable ETL needed to bring the data to a usable state. // Bio Richard Howes is a dedicated engineer who is passionate about control systems whether it be embedded systems, industrial automation, or AI/ML in a business application. All of these systems require a robust control philosophy that outlines the system, its environment, and how the controller should function within it. Richard has a bachelor's of Electrical Engineering from the University of Victoria where he specialized in industrial automation and embedded systems. Richard is primarily focused on the heavy industrial sectors like energy generation, oil & gas, pulp/paper, forestry, real estate, and manufacturing. He works on both physical process control and business process optimization using the control philosophy principles as a guiding star. Richard has been working with industrial systems for over 10 years designing, commissioning, operating, and maintaining automated systems. For the last 5 years, Richard has been investing time into the data and data science-related disciplines bringing the physical process as close as possible to the business taking advantage of disparate data sets throughout the organization. Now with the age of AI upon us, he is focusing on integrating this technology safely, reliably, and with distinct organizational goals and ROI. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links AWS Trainium: https://aws.amazon.com/machine-learning/trainium/ AWS Inferentia: https://aws.amazon.com/machine-learning/inferentia/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Richard on LinkedIn: https://www.linkedin.com/in/richardhowes/…

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Declarative Machine Learning Systems: Big Tech Level ML Without a Big Tech Team // Piero Molino // MLOps Coffee Sessions #101