Handling Multi-Terabyte LLM Checkpoints // Simon Karasik // #228

MLOps.community

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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

Netflix Sports Club Podcast

1
Paul Finebaum's 2025 Natty Pick & Diego Pavia's Heisman Hopes🏆 35:51

13 روز پیش35:51

پخش در آینده

لیست ها

پسندیدن

دوست داشته شد

35:51

Diego Pavia and Paul Finebaum are this week’s Netflix Sports Club guests. Paul Finebaum, the voice of the SEC, drops a bold national championship prediction that might give the Big Tenners pause, and he reveals which SEC quarterback has him starstruck. Vanderbilt quarterback, Diego Pavia, gives a standout performance on and off the field in SEC Football: Any Given Saturday. He relives that improbable ‘Bama victory, including pre-game routine and why he thinks this victory changed the conversation about Vandy football - sorry Nick Saban. Paul Fineman and Diego Pavia gaze into the crystal ball of the 2025 season, and what do they see? Heisman Trophies, making Auburn pay, and LSU’s chances for glory. In this interview, Vandy QB, Diego Pavia: 🟥 Details the 2024 Vandy vs. ‘Bama Buildup 🟥 Discusses playing for Coach Lea and with his bestie 🟥 Reveals his true height In this interview, SEC Expert, Paul Finebaum answers: 🟥 Why the SEC’s staying power is unrivaled? 🟥 Who’s almost a bigger star than Taylor Swift? 🟥 Which team will win the 2025 National Championship? 00:00 Intro 01:24 Vandy QB Diego Pavia Is a Star 01:37 Beating Bama 03:19 Choosing Vandy And Coach Lea 04:58 Give Me The Ball 06:20 Dude Put In The Work 07:38 Nick Saban Said What?? 08:20 Underdogs 10:22 Watch Out Auburn 12:33 Recruits: Come To Vandy! 13:29 Kay and Dani Talk SEC 15:54 Paul Finebaum’s SEC Picks 16:40 All About Arch Manning 17:23 SEC: The Only Game In Town 18:25 South Carolina Should Be In The Playoffs 19:40 Shane Beamer Bounce Back? 20:14 Arch Manning Is No Taylor Swift 21:42 The Weight of Being a Manning 22:31 Finebaum: “Best Player Since Tim Tebow” 23:20 LSU QB1 + Championship Or Bust 25:39 Post Saban Alabama 27:42 Is Vanderbilt Legit? 28:44 Can Mississippi State Survive? 29:49 The Vols “Will Struggle This Year” 31:44 SEC: Natty. Little Tenners: Nothing 33:09 Outro 34:28 Up Next - America’s Team: The Gambler And His Cowboys 🏈 Diego Pavia Instagram - https://bit.ly/45uLND4 TikTok - http://bit.ly/4fsLY5p X - https://bit.ly/4ldwx2j 🏈 Vanderbilt Instagram - http://bit.ly/4lfo8eu X - http://bit.ly/4mCiaWt YouTube - @vucommodores 🏈 Paul Finebaum Instagram - https://bit.ly/45kVtPv X - https://bit.ly/46JQFWc 🎙️ Kay Adams Instagram - http://bit.ly/3GYp4Go TikTok - http://bit.ly/4m7KmR9 X - http://bit.ly/45nI2Ou 🎙️ Dani Klupenger Instagram - https://bit.ly/3HeGGxx TikTok - https://bit.ly/4lQSBkl X - https://bit.ly/4lWpufr 🟥 Netflix Sports Instagram - http://bit.ly/45CPAhL TikTok - http://bit.ly/4mti6Ia X - http://bit.ly/4mseqGH Facebook - http://bit.ly/45o5xqK YouTube - @NetflixSports We want to hear from you! Leave us a voice message at www.speakpipe.com/NetflixSportsClub Be sure to watch, listen, and subscribe to the Netflix Sports Club Podcast on YouTube, Spotify, Tudum, and wherever you get your podcasts. Hosted by Kay Adams, the Netflix Sports Club Podcast is an all-access deep dive into the Netflix Sports Universe. Join Kay as she speaks with athletes, coaches, and top sports correspondents to break down the latest Netflix Sports series. Expect bold opinions, insightful analysis, and candid conversations you won’t find anywhere else.…

حدود یک سال پیش 55:36

MP3•خانه قسمت

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com

Simon Karasik⁠ is a proactive and curious ML Engineer with 5 years of experience. Developed & deployed ML models at WEB and Big scale for Ads and Tax.Huge thank you to Nebius AI for sponsoring this episode. Nebius AI - https://nebius.ai/MLOps podcast #228 with Simon Karasik, Machine Learning Engineer at Nebius AI, Handling Multi-Terabyte LLM Checkpoints.// AbstractThe talk provides a gentle introduction to the topic of LLM checkpointing: why is it hard, how big are the checkpoints. It covers various tips and tricks for saving and loading multi-terabyte checkpoints, as well as the selection of cloud storage options for checkpointing.// BioFull-stack Machine Learning Engineer, currently working on infrastructure for LLM training, with previous experience in ML for Ads, Speech, and Tax.// MLOps Jobs board https://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related Links --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Simon on LinkedIn: https://www.linkedin.com/in/simon-karasik/Timestamps:[00:00] Simon's preferred beverage[01:23] Takeaways[04:22] Simon's tech background[08:42] Zombie models garbage collection[10:52] The road to LLMs[15:09] Trained models Simon worked on[16:26] LLM Checkpoints[20:36] Confidence in AI Training[22:07] Different Checkpoints[25:06] Checkpoint parts [29:05] Slurm vs Kubernetes[30:43] Storage choices lessons[36:02] Paramount components for setup[37:13] Argo workflows[39:49] Kubernetes node troubleshooting[42:35] Cloud virtual machines have pre-installed mentoring[45:41] Fine-tuning[48:16] Storage, networking, and complexity in network design[50:56] Start simple before advanced; consider model needs.[53:58] Join us at our first in-person conference on June 25 all about AI Quality

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Handling Multi-Terabyte LLM Checkpoints // Simon Karasik // #228

MLOps.community

56 subscribers

published حدود یک سال پیش

اشتراک گذاری

MP3•خانه قسمت

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com

460 قسمت

All episodes

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…

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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…

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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…

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

۸ weeks پیش1:37:22

1:37:22

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

1
Greg Kamradt: Benchmarking Intelligence | ARC Prize 48:30

۸ weeks پیش48:30

48:30

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

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

۹ weeks 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

۹ weeks پیش1:09:37

1:09:37

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

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Everything Hard About Building AI Agents Today 47:02

10 weeks پیش47:02

47:02

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

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

10 weeks پیش54:01

54:01

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

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

10 weeks پیش55:30

55:30

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

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

11 weeks پیش48:42

48:42

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

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

11 weeks 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

12 weeks 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

13 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! // Bio Mohan 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 Links Websites: 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…

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

13 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: /sampartee Timestamps: [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)…

1
AI in M&A: Building, Buying, and the Future of Dealmaking // Kison Patel // #315 55:32

14 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 // Abstract The 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 Links Website: https://dealroom.net https://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 Timestamps: [00:00] Kison's preferred coffee [00:11] Takeaways [00:40] Founders Journey Slumps [05:07] Jira for M&A [10:57] Overcoming Idea Paralysis [14:32] Customer-led Discovery Success [22:20] Legal Fees in Deals [26:24] Data Room Differentiators [29:26] PLG vs Sales Teams [31:43] AI Pricing Strategies [35:15] PLG AI Cost Optimization [40:53] Building AI Teams [47:40] Great Companies Are Bought [51:10] M&A Failures and Fever [54:23] Wrap up…

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

14 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…

1
MLOps with Databricks // Maria Vechtomova // #314 52:43

14 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-vechtomova Timestamps: [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…

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/…

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MLOps.community « » Handling Multi-Terabyte LLM Checkpoints // Simon Karasik // #228

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Handling Multi-Terabyte LLM Checkpoints // Simon Karasik // #228