MLOps Coffee Sessions #7 // MLOps and DevOps - Parallels and Deviations // Featuring Damian Brady

MLOps.community

Player FM - Internet Radio Done Right

56 subscribers

اضافه شده در four سال پیش

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

Species Unite

1
Edita Birnkrant and Tracy Winston: The Horse Who Collapsed in the Street 37:03

۸ روز پیش37:03

پخش در آینده

لیست ها

پسندیدن

دوست داشته شد

37:03

“I could be walking in Central Park and come up on one of these horse and buggies. I don't think twice about it because I see it as part of the New York attraction. You know, you have the Statue of Liberty, you have Times Square, and you have these romantic horse and buggy things where people get married in the park and they ride these carriages. And tourists, they take these rides in Central Park. It's romantic, it's something beautiful to see. But I never thought for one second that these horses are abused.” – Tracy Winston, juror from Ryder’s trial New York City has a big, visible animal cruelty issue: horses forced to pull carriages, carrying heavy loads for long hours in all types of weather in the middle of chaotic traffic. Three years ago, a carriage horse named Ryder was a victim of this cruelty. He collapsed on a Manhattan street after being worked for hours in the summer heat. Two months later, he was euthanized. His story sparked global outrage. Ryder’s driver, Ian McKeever, was charged with animal cruelty The trial took place a few weeks ago, but McKeever was ultimately acquitted. This conversation is with Edita Birnkrant, the Executive Director of NYCLASS and Tracy Winston, one of the jurors from Ryder's trial. New York’s weak and outdated animal protection laws have not changed since Ryder died— and because of this, another avoidable death that occurred just a week after we recorded this interview. On August 5th, a horse named Lady died while pulling a carriage in Manhattan. This conversation is about accountability, about corruption and about what happens when justice fails the most vulnerable. It's too late for Ryder and Lady. But it is not too late to act. If you live in New York, please call your City Council members and tell them it’s time to bring Ryder’s Law, Intro 967, up for a vote and pass this vital bill to protect carriage horses from suffering and death on the city’s streets. To find your council member, go to: https://www.speciesunite.com/ny-horse-carriage-petition NYCLASS: https://nyclass.org/…

۵ سال پیش 55:31

MP3•خانه قسمت

MLOps and DevOps have a large number of parallels. Many of the techniques, practices, and processes used for traditional software projects can be followed almost exactly in ML projects. However, the day-to-day of an ML project is usually significantly different from a traditional software project. So while the ideas and principles can still apply, it’s important to be aware of the core aims of DevOps when applying them.

Damian is a Cloud Advocate specializing in DevOps and MLOps. After spending a year in Toronto, Canada, he returned to Australia - the land of the dangerous creatures and beautiful beaches - in 2018. Formerly a dev at Octopus Deploy and a Microsoft MVP, he has a background in software development and consulting in a broad range of industries. In Australia, he co-organised the Brisbane .Net User Group, and launched the now annual DDD Brisbane conference. He regularly speaks at conferences, User Groups, and other events around the world. Most of the time you'll find him talking to software engineers, IT pros and managers to help them get the most out of their DevOps strategies.

|| Links Referenced in the Show ||
MLOps, or DevOps for Machine Learning: https://damianbrady.com.au/2019/10/28/mlops-or-devops-for-machine-learning/
Microsoft Azure Machine Learning: http://ml.azure.com/
MLOps Coffee Sessions #6 Continuous Integration for ML // Featuring Elle O'Brien: https://www.youtube.com/watch?v=L98VxJDHXMM
MLOps: Isn’t that just DevOps? Ryan Dawson speaks at MLOps Coffee Session: https://www.seldon.io/mlops-isnt-that-just-devops-ryan-dawson-speaks-at-mlops-coffee-session/
DVC - Data Version Control: https://dvc.org/
Pachyderm - Version-controlled data science: https://www.pachyderm.com/
Databricks - Unified Data Analytics: https://databricks.com/

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://go.mlops.community/register

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Damian on LinkedIn: https://www.linkedin.com/in/damianbrady/

460 قسمت

MLOps Coffee Sessions #7 // MLOps and DevOps - Parallels and Deviations // Featuring Damian Brady

MLOps.community

56 subscribers

published ۵ سال پیش

اشتراک گذاری

MP3•خانه قسمت

460 قسمت

همه قسمت ها

1
Knowledge is Eventually Consistent // Devin Stein // #335 55:14

۴ روز پیش55:14

55:14

Knowledge is Eventually Consistent // MLOps Podcast #335 with Devin Stein, CEO of Dosu. Grateful to @Databricks and @hyperbolic-labs for supporting our podcast and helping us keep great conversations going. Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter // Abstract AI as a partner in building richer, more accessible written knowledge—so communities and teams can thrive, endure, and expand their reach. // Bio Devin is the CEO and Founder of Dosu. Prior to Dosu, Devin was an early engineer and leader at various startups. Outside of work, he is an active open source contributor and maintainer. // Related Links Website: https://github.com/devstein https://www.youtube.com/watch?v=sC8aW47DqPg https://www.youtube.com/watch?v=PuM0Gd3txfQ https://www.youtube.com/watch?v=ah6diDQ9wyw https://www.youtube.com/watch?v=x22FEQic8lg ~~~~~~~~ ✌️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 Devin on LinkedIn: /devstein/ Timestamps: [00:00] Devin's preferred coffee [00:53] Facts agent overview [03:47] Decision state detection [07:55 - 8:41] Databricks ad [08:42] Context-dependent word meanings [15:25] Fact lifecycle management [24:40] Maintaining quality documentation [30:10 - 31:06] Hyperbolic ad [31:07] Agent collaboration scenarios [38:22] Knowledge maintenance [44:10] Deployment and integration strategies [48:13] Flywheel data approach [51:54] Horror story engineering function [54:32] Wrap up…

1
LinkedIn Recommender System Predictive ML vs LLMs 47:39

۹ روز پیش47:39

47:39

Demetrios chats with Arpita Vats about how LLMs are shaking up recommender systems. Instead of relying on hand-crafted features and rigid user clusters, LLMs can read between the lines—spotting patterns in user behavior and content like a human would. They cover the perks (less manual setup, smarter insights) and the pain points (latency, high costs), plus how mixing models might be the sweet spot. From timing content perfectly to knowing when traditional methods still win, this episode pulls back the curtain on the future of recommendations. // Bio Arpita Vats is a passionate and accomplished researcher in the field of Artificial Intelligence, with a focus on Natural Language Processing, Recommender Systems, and Multimodal AI. With a strong academic foundation and hands-on experience at leading tech companies such as LinkedIn, Meta, and Staples, Arpita has contributed to cutting-edge projects spanning large language models (LLMs), privacy-aware AI, and video content understanding. She has published impactful research at premier venues and actively serves as a reviewer for top-tier conferences like CVPR, ICLR, and KDD. Arpita’s work bridges academic innovation with industry-scale deployment, making her a sought-after collaborator in the AI research community. Currently, she is engaged in exploring the alignment and safety of language models, developing robust metrics like the Alignment Quality Index (AQI), and optimizing model behavior across diverse input domains. Her dedication to advancing ethical and scalable AI reflects both in her academic pursuits and professional contributions. // Related Links #recommendersystems #LLMs #linkedin ~~~~~~~~ ✌️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 Arpita on LinkedIn: /arpita-v-0a14a422/ Timestamps: [00:00] Smarter Content Recommendations [05:19] LLMs: Next-Gen Recommendations [09:37] Judging LLM Suggestions [11:38] Old vs New Recommenders [14:11] Why LLMs Get Stuck [16:52] When Old Models Win [22:39] After-Booking Rec Magic [23:26] One LLM to Rule Models [29:14] Personalization That Evolves [32:39] SIM Beats Transformers in QA [35:35] Agents Writing Research Papers [37:12] Big-Company Agent Failures [41:47] LinkedIn Posts Fade Faster [46:04] Clustering Shifts Social Feeds [47:01] Vanishing Posts, Replay Mode…

1
GPU Considerations, Labeling Privacy, Rapid Fine Tuning, and the Role of Private Eval Pipelines to Benchmark New Models 55:46

12 روز پیش55:46

55:46

Agents in Production [Podcast Limited Series] Episode Nine – Training LLMs, Picking the Right Models, and GPU Headaches Paul van der Boor and Zulkuf Genc from Prosus join Demetrios to talk about what it really takes to get AI agents running in production. From building solid eval sets to juggling GPU logistics and figuring out which models are worth using (and when), they share hard-won lessons from the front lines. If you're working with LLMs at scale—or thinking about it—this one’s for you. Guest speakers: Paul van der Boor - VP AI at Prosus Group Zulkuf Genc - Director of AI at Prosus Group Host: Demetrios Brinkmann - Founder of MLOps Community ~~~~~~~~ ✌️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/ ]…

1
The Hidden Bottlenecks Slowing Down AI Agents 47:59

15 روز پیش47:59

47:59

Demetrios chats with Paul van der Boor and Bruce Martens from Prosus about the real bottlenecks in AI agent development—not tools, but evaluation and feedback. They unpack when to build vs. buy, the tradeoffs of external vendors, and how internal tools like Copilot are reshaping workflows. Guest speakers: Paul van der Boor - VP AI at Prosus Group Bruce Martens - AI Engineer at Prosus Group Host: Demetrios Brinkmann - Founder of MLOps Community ~~~~~~~~ ✌️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/ ]…

1
9 Commandments for Building AI Agents 1:20:33

20 روز پیش1:20:33

1:20:33

Building AI agents that actually get things done is harder than it looks. Demetrios, Paul, and Dmitri break down what makes agents effective—from smart planning and memory to treating tools, systems, and even people as components. They cover the "react" loop, budgeting for long tasks, sandboxing, and learning from experience. It’s a sharp, practical look at what it really takes to design useful, adaptive AI agents. Guest speakers: Paul van der Boor - VP AI at Prosus Group Dmitri Jarnikov - Senior Director of Data Science at Prosus Group Host: Demetrios Brinkmann - Founder of MLOps Community ~~~~~~~~ ✌️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/ ]…

1
Enterprise AI Adoption Challenges 1:05:00

23 روز پیش1:05:00

1:05:00

Building AI Agents that work is no small feat. In Agents in Production [Podcast Limited Series] - Episode Six, Paul van der Boor and Sean Kenny share how they scaled AI across 100+ companies with Toqan—a tool born from a Slack experiment and grown into a powerful productivity platform. From driving adoption and building super users to envisioning AI employees of the future, this conversation cuts through the hype and gets into what it really takes to make AI work in the enterprise. Guest speakers: Paul van der Boor - VP AI at Prosus Group Sean Kenny - Senior Product Manager at Prosus Group Host: Demetrios Brinkmann - Founder of MLOps Community ~~~~~~~~ ✌️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/ ]…

1
Real-time Feature Generation at Lyft // Rakesh Kumar // #334 58:04

26 روز پیش58:04

58:04

Real-time Feature Generation at Lyft // MLOps Podcast #334 with Rakesh Kumar, Senior Staff Software Engineer at Lyft. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract This session delves into real-time feature generation at Lyft. Real-time feature generation is critical for Lyft where accurate up-to-the-minute marketplace data is paramount for optimal operational efficiency. We will explore how the infrastructure handles the immense challenge of processing tens of millions of events per minute to generate features that truly reflect current marketplace conditions. Lyft has built this massive infrastructure over time, evolving from a humble start and a naive pipeline. Through lessons learned and iterative improvements, Lyft has made several trade-offs to achieve low-latency, real-time feature delivery. MLOps plays a critical role in managing the lifecycle of these real-time feature pipelines, including monitoring and deployment. We will discuss the practicalities of building and maintaining high-throughput, low-latency real-time feature generation systems that power Lyft’s dynamic marketplace and business-critical products. // Bio Rakesh Kumar is a Senior Staff Software Engineer at Lyft, specializing in building and scaling Machine Learning platforms. Rakesh has expertise in MLOps, including real-time feature generation, experimentation platforms, and deploying ML models at scale. He is passionate about sharing his knowledge and fostering a culture of innovation. This is evident in his contributions to the tech community through blog posts, conference presentations, and reviewing technical publications. // Related Links Website: https://englife101.io/ https://eng.lyft.com/search?q=rakesh https://eng.lyft.com/real-time-spatial-temporal-forecasting-lyft-fa90b3f3ec24 https://eng.lyft.com/evolution-of-streaming-pipelines-in-lyfts-marketplace-74295eaf1eba Streaming Ecosystem Complexities and Cost Management // Rohit Agrawal // MLOps Podcast #302 - https://youtu.be/0axFbQwHEh8 ~~~~~~~~ ✌️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 Rakesh on LinkedIn: /rakeshkumar1007/ Timestamps: [00:00] Rakesh preferred coffee [00:24] Real-time machine learning [04:51] Latency tricks explanation [09:28] Real-time problem evolution [15:51] Config management complexity [18:57] Data contract implementation [23:36] Feature store [28:23] Offline vs online workflows [31:02] Decision-making in tech shifts [36:54] Cost evaluation frequency [40:48] Model feature discussion [49:09] Hot shard tricks [55:05] Pipeline feature bundling [57:38] Wrap up…

1
AI Agent Development Tradeoffs You NEED to Know 57:06

30 روز پیش57:06

57:06

Sherwood Callaway, tech lead at 11X, joins us to talk about building digital workers—specifically Alice (an AI sales rep) and Julian (a voice agent)—that are shaking up sales outreach by automating complex, messy tasks. He looks back on his YC days at OpKit, where he first got his hands dirty with voice AI, and compares the wild ride of building voice vs. text agents. We get into the use of Langgraph Cloud, integrating observability tools like Langsmith and Arize, and keeping hallucinations in check with regular Evals. Sherwood and Demetrios wrap up with a look ahead: will today's sprawling AI agent stacks eventually simplify? // Bio Sherwood Callaway is an emerging leader in the world of AI startups and AI product development. He currently serves as the first engineering manager at 11x, a series B AI startup backed by Benchmark and Andreessen Horowitz, where he oversees technical work on "Alice", an AI sales rep that outperforms top human SDRs. Alice is an advanced agentic AI working in production and at scale. Under Sherwood’s leadership, the system grew from initial prototype to handling over 1 million prospect interactions per month across 300+ customers, leveraging partnerships with OpenAI, Anthropic, and LangChain while maintaining consistent performance and reliability. Alice is now generating eight figures in ARR. Sherwood joined 11x in 2024 through the acquisition of his YC-backed startup, Opkit, where he built and commercialized one of the first-ever AI phone calling solutions for a specific industry vertical (healthcare). Prior to Opkit, he was the second infrastructure engineer at Brex, where he designed, built, and scaled the production infrastructure that supported Brex’s application and engineering org through hypergrowth. He currently lives in San Francisco, CA. // Related Links ~~~~~~~~ ✌️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 Sherwood on LinkedIn: /sherwoodcallaway/ #aiengineering Timestamps: [00:00] AI Takes Over Health Calls [05:05] What Can Agents Really Do? [08:25] Who’s in Charge—User or Agent? [11:20] Why Graphs Matter in Agents [15:03] How Complex Should Agents Be? [18:33] The Hidden Cost of Model Upgrades [21:57] Inside the LLM Agent Loop [25:08] Turning Agents into APIs [29:06] Scaling Agents Without Meltdowns [30:04] The Monorepo Tangle, Explained [34:01] Building Agents the Open Source Way [38:49] What Production-Ready Agents Look Like [41:23] AI That Fixes Code on Its Own [43:26] Tracking Agent Behavior with OpenTelemetry [46:43] Running Agents Locally with Phoenix [52:55] LangGraph Meets Arise for Agent Control [53:29] Hunting Hallucinations in Agent Traces [56:45] Off-Script Insights Worth Hearing…

1
From the Legal Trenches to Tech // Nick Coleman // #332 35:51

۵ weeks پیش35:51

35:51

From the Legal Trenches to Tech // MLOps Podcast #332 with Nick Coleman, Attorney/Founder of LexMed. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Nick Coleman shares his journey from high-volume Social Security disability practice to founding LexMed, a legal tech startup leveraging AI to transform how attorneys handle complex cases. He'll discuss LexMed's dual AI platforms: Hearing Echo, which automates transcription and analysis of disability hearings with speaker identification and critical testimony validation, and ChartVision, which combines human medical abstraction with AI to extract and map medical evidence to disability criteria. Nick will explain how "vibe coding" has dramatically reduced friction between his subject matter expertise and technical implementation, enabling rapid prototyping that preserves legal insights through development. By bridging domain knowledge and technology, LexMed has created solutions that address the real-world challenges he experienced firsthand in his high-volume disability practice, offering valuable lessons for AI implementation in other specialized fields. // Bio Nick Coleman is the founder and CEO of LexMed, a legal tech startup applying advanced AI to transform the practice of law. As a Social Security disability attorney with extensive appellate experience, Nick identified critical inefficiencies in legal workflows that technology could solve. LexMed's flagship product, Hearing Echo, leverages speech recognition and natural language processing to automate the transcription and analysis of disability hearing audio, dramatically improving case management for attorneys. Nick holds an AV Preeminent rating from Martindale-Hubbell, has been recognized as a Super Lawyers Rising Star, and serves on the Arkansas Bar Artificial Intelligence Task Force. With deep expertise at the intersection of law and technology, Nick is passionate about democratizing access to justice through innovative AI solutions. // Related Links Website: www.lexmed.ai ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [ https://go.mlops.community/slack ] Follow us on X/Twitter [ @mlopscommunity ]( https://x.com/mlopscommunity ) or [LinkedIn]( https://go.mlops.community/linkedin )] Sign up for the next meetup: [ https://go.mlops.community/register ] MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Nick on LinkedIn: /nicklcoleman/ Timestamps: [00:00] Disability Claims Advocacy [00:29] AI Native Startup [02:08] Disability Claims Process [07:56] Tech Journey [10:52] AI in Document Review [13:57] Building a Case for Appeal [19:26] Medical Claims Language Model [23:37] Tech-Driven Compliance Solutions [30:31] Claim Prioritization Strategy [34:57] Wrap up…

1
The Rise of Sovereign AI and Global AI Innovation in a World of US Protectionism // Frank Meehan // MLOps Podcast #331 54:13

۶ weeks پیش54:13

54:13

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

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

۶ weeks پیش1:04:49

1:04:49

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

1
Inside Uber’s AI Revolution - Everything about how they use AI/ML 45:23

۷ weeks پیش45:23

45:23

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

1
The Missing Data Stack for Physical AI 52:42

۷ weeks پیش52:42

52:42

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

29 weeks پیش1:00:36

1:00:36

In his 13 years of software engineering, Ilya Reznik has specialized in commercializing machine learning solutions and building robust ML platforms. He's held technical lead and staff engineering roles at premier firms like Adobe, Twitter, and Meta. Currently, Ilya channels his expertise into his travel startup, Jaunt, while consulting and advising emerging startups. Navigating Machine Learning Careers: Insights from Meta to Consulting // MLOps Podcast #286 with Ilya Reznik, ML Engineering Thought Leader at Instructed Machines, LLC. // Abstract Ilya Reznik's insights into machine learning and career development within the field. With over 13 years of experience at leading tech companies such as Meta, Adobe, and Twitter, Ilya emphasizes the limitations of traditional model fine-tuning methods. He advocates for alternatives like prompt engineering and knowledge retrieval, highlighting their potential to enhance AI performance without the drawbacks associated with fine-tuning. Ilya's recent discussions at the NeurIPS conference reflect a shift towards practical applications of Transformer models and innovative strategies like curriculum learning. Additionally, he shares valuable perspectives on navigating career progression in tech, offering guidance for aspiring ML engineers aiming for senior roles. His narrative serves as a blend of technical expertise and practical career advice, making it a significant resource for professionals in the AI domain. // Bio Ilya has navigated a diverse career path since 2011, transitioning from physicist to software engineer, data scientist, ML engineer, and now content creator. He is passionate about helping ML engineers advance their careers and making AI more impactful and beneficial for society. Previously, Ilya was a technical lead at Meta, where he contributed to 12% of the company’s revenue and managed approximately 30 production ML models. He also worked at Twitter, overseeing offline model evaluation, and at Adobe, where his team was responsible for all intelligent services within Adobe Analytics. Based in Salt Lake City, Ilya enjoys the outdoors, tinkering with Arduino electronics, and, most importantly, spending time with his family. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: mlepath.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 Ilya on LinkedIn: https://www.linkedin.com/in/ibreznik/…

به Player FM خوش آمدید!

Player FM در سراسر وب را برای یافتن پادکست های با کیفیت اسکن می کند تا همین الان لذت ببرید. این بهترین برنامه ی پادکست است که در اندروید، آیفون و وب کار می کند. ثبت نام کنید تا اشتراک های شما در بین دستگاه های مختلف همگام سازی شود.

به بیش از 500 موضوع گوش کنید

56 subscribers

Minecraft

Mini Mic Pro (Latest Model) - Professional Wireless Microphone for iPhone, iPad, Android, Lavalier Microphone for Video Recording - iPhone Mic Crystal Clear Recording with USB-C for Content Creators

Ailun 3 Pack Screen Protector for iPhone 16 Pro Max [6.9 inch] + 3 Pack Camera Lens Protector with Installation Frame,Sensor Protection,Dynamic Island Compatible,Case Friendly Tempered Glass Film

پادکست هایی که ارزش شنیدن دارند

MLOps.community « » MLOps Coffee Sessions #7 // MLOps and DevOps - Parallels and Deviations // Featuring Damian Brady

MLOps Coffee Sessions #7 // MLOps and DevOps - Parallels and Deviations // Featuring Damian Brady

پادکست هایی که ارزش شنیدن دارند

به Player FM خوش آمدید!

Elmer's Disappearing Purple School Glue Sticks Washable 6 Grams 12 Count

The Let Them Theory: A Life-Changing Tool That Millions of People Can't Stop Talking About

Amazon Basics Multipurpose Copy Printer Paper, 8.5 x 11 Inches, 20 lb, 1 Ream, (500 Sheets), 92 Bright, White

Bounty Paper Towels Quick Size, White, 16 Family Rolls = 40 Regular Rolls

The Let Them Theory: A Life-Changing Tool That Millions of People Can't Stop Talking About

راهنمای مرجع سریع

MLOps.community « »
MLOps Coffee Sessions #7 // MLOps and DevOps - Parallels and Deviations // Featuring Damian Brady