MLOps meetup #12 // Why Data Scientists Should Know Data Engineering with Dan Sullivan

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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

The Big Pitch with Jimmy Carr

1
Romesh Ranganathan Pitches a Tearjerker with a Curse, a Meet Cute, and an Orangutan! 33:06

13 روز پیش33:06

پخش در آینده

لیست ها

پسندیدن

دوست داشته شد

33:06

Our guest this week is Romesh Rangathan! And his subgenre is… BRB Crying. Will Romesh deliver a beautiful and moving film pitch that makes Jimmy break down crying? Or will his idea be a fever dream of orangutans, ancient curses and having sex in front of animals? Listen in to find out! Listen on all podcast platforms and watch on the Netflix is a Joke YouTube Channel. The Big Pitch is a co-production by Netflix and BBC Studios Audio. Jimmy Carr is an award-winning stand-up comedian and writer, touring his brand-new show JIMMY CARR: LAUGHS FUNNY throughout the USA from May to November this year, as well as across the UK and Europe, before hitting Australia and New Zealand in early 2026. All info and tickets for the tour are available at JIMMYCARR.COM Production Coordinator: Becky Carewe-Jeffries Production Manager: Mabel Finnegan-Wright Editor: Stuart Reid Producer: Pete Strauss Executive Producer: Richard Morris Executive Producers for Netflix: Kathryn Huyghue, Erica Brady, and David Markowitz Set Design: Helen Coyston Studios: Tower Bridge Studios Make Up: Kit Hall Cameras: Daniel Spencer Sound: Charlie Emery Branding: Tim Lane Photography: James Hole Images supplied via Getty Images…

۵ سال پیش 59:51

MP3•خانه قسمت

MLOps meetup #12 // What are the advantages for a data scientist to know data engineering?

What good is learning Data Engineering skills? These days full stack is overflowing with all the different things you need to know about so why learn data Engineering now? Our guest on this meetup will make the case for what the advantages are if you do decide to learn data engineering and also go into depth on how to do data engineering in the cloud.

Dan Sullivan is a software architect and data scientist with extensive experience in big data, machine learning, data architecture, security, stream processing, and cloud architecture. Dan is the author of the official Google Cloud study guides for the Professional Architect, Professional Data Engineer, and Associate Cloud Engineer exam guides as well as NoSQL for Mere Mortals.

He is also the author of over ten LinkedIn Learning courses on data science, machine learning, SQL, data architecture, and NoSQL. He holds a Ph.D. in genetics, bioinformatics, and computational biology.

Get a copy of his new book here: https://www.wiley.com/en-us/Official+Google+Cloud+Certified+Professional+Data+Engineer+Study+Guide-p-9781119618454
Join our slack community: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw
Follow us on twitter:@mlopscommunity
Sign up for the next meetup: https://zoom.us/webinar/register/WN_a_nuYR1xT86TGIB2wp9B1g
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Dan Sullivan on LinkedIn: https://www.linkedin.com/in/dansullivanpdx/

445 قسمت

MLOps meetup #12 // Why Data Scientists Should Know Data Engineering with Dan Sullivan

MLOps.community

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published ۵ سال پیش

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MP3•خانه قسمت

MLOps meetup #12 // What are the advantages for a data scientist to know data engineering?

445 قسمت

همه قسمت ها

1
Bridging the Gap Between AI and Business Data // Deepti Srivastava // #325 57:13

۳ 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

۶ روز 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

10 روز 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_pagehttps://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

13 روز 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

13 روز 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

17 روز 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?…

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Product Metrics are LLM Evals // Raza Habib CEO of Humanloop // #320 53:06

21 روز پیش53:06

53:06

Raza Habib, the CEO of LLM Eval platform Humanloop , talks to us about how to make your AI products more accurate and reliable by shortening the feedback loop of your evals. Quickly iterating on prompts and testing what works, along with some of his favorite Dario from Anthropic AI Quotes. // Bio Raza is the CEO and Co-founder at Humanloop. He has a PhD in Machine Learning from UCL, was the founding engineer of Monolith AI, and has built speech systems at Google. For the last 4 years, he has led Humanloop and supported leading technology companies such as Duolingo, Vanta, and Gusto to build products with large language models. Raza was featured in the Forbes 30 Under 30 technology list in 2022, and Sifted recently named him one of the most influential Gen AI founders in Europe. // Related Links Websites: https://humanloop.com ~~~~~~~~ ✌️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 Raza on LinkedIn: /humanloop-raza Timestamps: [00:00] Cracking Open System Failures and How We Fix Them [05:44] LLMs in the Wild — First Steps and Growing Pains [08:28] Building the Backbone of Tracing and Observability [13:02] Tuning the Dials for Peak Model Performance [13:51] From Growing Pains to Glowing Gains in AI Systems [17:26] Where Prompts Meet Psychology and Code [22:40] Why Data Experts Deserve a Seat at the Table [24:59] Humanloop and the Art of Configuration Taming [28:23] What Actually Matters in Customer-Facing AI [33:43] Starting Fresh with Private Models That Deliver [34:58] How LLM Agents Are Changing the Way We Talk [39:23] The Secret Lives of Prompts Inside Frameworks [42:58] Streaming Showdowns — Creativity vs. Convenience [46:26] Meet Our Auto-Tuning AI Prototype [49:25] Building the Blueprint for Smarter AI [51:24] Feedback Isn’t Optional — It’s Everything…

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Getting AI Apps Past the Demo // Vaibhav Gupta // #319 50:29

24 روز پیش50:29

50:29

Getting AI Apps Past the Demo // MLOps Podcast #319 with Vaibhav Gupta, CEO of BoundaryML. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract It's been two years, and we still seem to see AI disproportionately more in demos than production features. Why? And how can we apply engineering practices we've all learned in the past decades to our advantage here? // Bio Vaibhav is one of the creators of BAML and a YC alum. He spent 10 years in AI performance optimization at places like Google, Microsoft, and D.E. Shaw. He loves diving deep and chatting about anything related to Gen AI and Computer Vision! // Related Links Website: https://www.boundaryml.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 Vaibhav on LinkedIn: /vaigup Timestamps: [00:00] Vaibhav's preferred coffee [00:38] What is BAML [03:07] LangChain Overengineering Issues [06:46] Verifiable English Explained [11:45] Python AI Integration Challenges [15:16] Strings as First-Class Code [21:45] Platform Gap in Development [30:06] Workflow Efficiency Tools [33:10] Surprising BAML Insights [40:43] BAML Cool Projects [45:54] BAML Developer Conversations [48:39] Wrap up…

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Building Out GPU Clouds // Mohan Atreya // #317 47:57

۴ weeks پیش47:57

47:57

Demetrios and Mohan Atreya break down the GPU madness behind AI — from supply headaches and sky-high prices to the rise of nimble GPU clouds trying to outsmart the giants. They cover power-hungry hardware, failed experiments, and how new cloud models are shaking things up with smarter provisioning, tokenized access, and a whole lotta hustle. It's a wild ride through the guts of AI infrastructure — fun, fast, and full of sparks! Big thanks to the folks at Rafay for backing this episode — appreciate the support in making these conversations happen! // BioMohan is a seasoned and innovative product leader currently serving as the Chief Product Officer at Rafay Systems. He has led multi-site teams and driven product strategy at companies like Okta, Neustar, and McAfee. // Related LinksWebsites: https://rafay.co/ ~~~~~~~~ ✌️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 Mohan on LinkedIn: /mohanatreya Timestamps: [00:00] AI/ML Customer Challenges [04:21] Dependency on Microsoft for Revenue [09:08] Challenges of Hypothesis in AI/ML [12:17] Neo Cloud Onboarding Challenges [15:02] Elastic GPU Cloud Automation [19:11] Dynamic GPU Inventory Management [20:25] Terraform Lacks Inventory Awareness [26:42] Onboarding and End-User Experience Strategies [29:30] Optimizing Storage for Data Efficiency [33:38] Pizza Analogy: User Preferences [35:18] Token-Based GPU Cloud Monetization [39:01] Empowering Citizen Scientists with AI [42:31] Innovative CFO Chatbot Solutions [47:09] Cloud Services Need Spectrum…

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A Candid Conversation Around MCP and A2A // Rahul Parundekar and Sam Partee // #316 SF Live 1:04:42

۵ weeks پیش1:04:42

1:04:42

Demetrios, Sam Partee, and Rahul Parundekar unpack the chaos of AI agent tools and the evolving world of MCP (Model Context Protocol). With sharp insights and plenty of laughs, they dig into tool permissions, security quirks, agent memory, and the messy path to making agents actually useful. // Bio Sam Partee Sam Partee is the CTO and Co-Founder of Arcade AI. Previously a Principal Engineer leading the Applied AI team at Redis, Sam led the effort in creating the ecosystem around Redis as a vector database. He is a contributor to multiple OSS projects including Langchain, DeterminedAI, LlamaIndex and Chapel amongst others. While at Cray/HPE he created the SmartSim AI framework which is now used at national labs around the country to integrate HPC simulations like climate models with AI. Rahul Parundekar Rahul Parundekar is the founder of AI Hero. He graduated with a Master's in Computer Science from USC Los Angeles in 2010, and embarked on a career focused on Artificial Intelligence. From 2010-2017, he worked as a Senior Researcher at Toyota ITC working on agent autonomy within vehicles. His journey continued as the Director of Data Science at FigureEight (later acquired by Appen), where he and his team developed an architecture supporting over 36 ML models and managing over a million predictions daily. Since 2021, he has been working on AI Hero, aiming to democratize AI access, while also consulting on LLMOps(Large Language Model Operations), and AI system scalability. Other than his full time role as a founder, he is also passionate about community engagement, and actively organizes MLOps events in SF, and contributes educational content on RAG and LLMOps at learn.mlops.community. // Related Links Websites: arcade.dev aihero.studio~~~~~~~~ ✌️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 Rahul on LinkedIn: /rparundekar Connect with Sam on LinkedIn: /samparteeTimestamps:[00:00] Agents & Tools, Explained (Without Melting Your Brain) [09:51] MVP Servers: Why Everything’s on Fire (and How to Fix It) [13:18] Can We Actually Trust the Protocol? [18:13] KYC, But Make It AI (and Less Painful) [25:25] Web Automation Tests: The Bugs Strike Back [28:18] MCP Dev: What Went Wrong (and What Saved Us) [33:53] Social Login: One Button to Rule Them All [39:33] What Even Is an AI-Native Developer? [42:21] Betting Big on Smarter Models (High Risk, High Reward) [51:40] Harrison’s Bold New Tactic (With Real-Life Magic Tricks) [55:31] Async Task Handoffs: Herding Cats, But Digitally [1:00:37] Getting AI to Actually Help Your Workflow [1:03:53] The Infamous Varma System Error (And How We Dodge It)…

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AI in M&A: Building, Buying, and the Future of Dealmaking // Kison Patel // #315 55:32

۵ weeks پیش55:32

55:32

AI in M&A: Building, Buying, and the Future of Dealmaking // MLOps Podcast #315 with Kison Patel, CEO and M&A Science at DealRoom . Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractThe intersection of M&A and AI, exploring how the DealRoom team developed AI capabilities and the practical use cases of AI in dealmaking. Discuss the evolving landscape of AI-driven M&A, the factors that make AI companies attractive acquisition targets, and the key indicators of success in this space. // Bio Kison Patel is the Founder and CEO of DealRoom, an M&A lifecycle management platform designed for buyer-led M&A and recognized twice on the Inc. 5000 Fastest Growing Companies list. He also founded M&A Science, a global community offering courses, events, and the top-rated M&A Science podcast with over 2.25 million downloads. Through the podcast, Kison shares actionable insights from top M&A experts, helping professionals modernize their approach to deal-making. He is also the author of *Agile M&A: Proven Techniques to Close Deals Faster and Maximize Value*, a guide to tech-enabled, adaptive M&A practices. Kison is dedicated to disrupting traditional M&A with innovative tools and education, empowering teams to drive greater efficiency and value. // Related LinksWebsite: https://dealroom.nethttps://www.mascience.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 Kison on LinkedIn: /kisonpatel…

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AI, Marketing, and Human Decision Making // Fausto Albers // #313 49:40

۶ weeks پیش49:40

49:40

AI, Marketing, and Human Decision Making // MLOps Podcast #313 with Fausto Albers, AI Engineer & Community Lead at AI Builders Club. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Demetrios and Fausto Albers explore how generative AI transforms creative work, decision-making, and human connection, highlighting both the promise of automation and the risks of losing critical thinking and social nuance. // Bio Fausto Albers is a relentless explorer of the unconventional—a techno-optimist with a foundation in sociology and behavioral economics, always connecting seemingly absurd ideas that, upon closer inspection, turn out to be the missing pieces of a bigger puzzle. He thrives in paradox: he overcomplicates the simple, oversimplifies the complex, and yet somehow lands on solutions that feel inevitable in hindsight. He believes that true innovation exists in the tension between chaos and structure—too much of either, and you’re stuck. His career has been anything but linear. He’s owned and operated successful restaurants, served high-stakes cocktails while juggling bottles on London’s bar tops, and later traded spirits for code—designing digital waiters, recommender systems, and AI-driven accounting tools. Now, he leads the AI Builders Club Amsterdam, a fast-growing community where AI engineers, researchers, and founders push the boundaries of intelligent systems. Ask him about RAG, and he’ll insist on specificity—because, as he puts it, discussing retrieval-augmented generation without clear definitions is as useful as declaring that “AI will have an impact on the world.” An engaging communicator, a sharp systems thinker, and a builder of both technology and communities, Fausto is here to challenge perspectives, deconstruct assumptions, and remix the future of AI. // Related Links Website: aibuilders.club Moravec's paradox: https://en.wikipedia.org/wiki/Moravec%27s_paradox?utm_source=chatgpt.com Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311: https://youtu.be/jJXee5rMtHI ~~~~~~~~ ✌️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 Fausto on LinkedIn: /stepintoliquid Timestamps:[00:00] Fausto's preferred coffee[00:26] Takeaways[01:18] Automated Ad Creative Generation[07:14] AI in Marketing Workflows[13:23] MCP and System Bottlenecks[21:45] Forward Compatibility vs Optimization[29:57] Unlocking Workflow Speed[33:48] AI Dependency vs Critical Thinking[37:44] AI Realism and Paradoxes[42:30] Outsourcing Decision-Making Risks[46:22] Human Value in Automation[49:02] Wrap up…

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MLOps with Databricks // Maria Vechtomova // #314 52:43

۶ weeks پیش52:43

52:43

MLOps with Databricks // MLOps Podcast #314 with Maria Vechtomova, MLOps Tech Lead | Founder at Ahold Delhaize | Marvelous MLOps. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract The world of MLOps is very complex as there is an endless amount of tools serving its purpose, and it is very hard to get your head around it. Instead of combining various tools and managing them, it may make sense to opt for a platform instead. Databricks is a leading platform for MLOps. In this discussion, I will explain why it is the case, and walk you through Databricks MLOps features. // Bio Maria is an MLOps Tech lead with over 10 years of experience in Data and AI. For the last 8 years, Maria has focused on MLOps and helped to establish MLOps best practices at large corporations. Together with her colleague, she co-founded Marvelous MLOps to share knowledge on MLOps via training, social media posts, and blogs. // Related Links Website: marvelousmlops.io MLOps Course discount code: MLOPS100 for the podcast listeners - https://maven.com/marvelousmlops/mlops-with-databricks?promoCode=MLOPS100 ~~~~~~~~ ✌️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 Maria on LinkedIn: /maria-vechtomovaTimestamps: [00:00] Maria's preferred coffee[00:42] Takeaways[02:48] Why Databricks for MLOps[09:56] Platform Adoption vs Procurement Pain[12:56] Databricks Best Practices[16:57] Feature Store Overview[22:00] Managed system trade-offs[29:15] Databricks Developments and Trends[44:31] Insider Info and Summit[45:47] Data Ownership Pros and Cons[48:08] Data Contracts and Challenges[51:25] MLOps Databricks Book Guide[52:19] Wrap up…

23 weeks پیش47:07

47:07

MLOps.community

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

24 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/…

MLOps.community

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

24 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…

MLOps.community

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

26 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/…

MLOps.community

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

26 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…

MLOps.community

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

27 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…

MLOps.community

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

28 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/…

MLOps.community

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

29 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/…

MLOps.community

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

29 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/…

MLOps.community

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

30 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…

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

30 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

31 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

31 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

32 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

33 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

35 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

36 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

36 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

36 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

37 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

37 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

38 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

38 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

1
Global Feature Store // Gottam Sai Bharath & Cole Bailey // #263 50:18

39 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

40 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

40 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

40 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

1
Ax a New Way to Build Complex Workflows with LLMs // Vikram Rangnekar // #259 53:25

41 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…

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MLOps.community « » MLOps meetup #12 // Why Data Scientists Should Know Data Engineering with Dan Sullivan

MLOps meetup #12 // Why Data Scientists Should Know Data Engineering with Dan Sullivan

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MLOps meetup #12 // Why Data Scientists Should Know Data Engineering with Dan Sullivan