MLOps.community

Demetrios

Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)

  1. Knowledge is Eventually Consistent // Devin Stein // #335

    4일 전

    Knowledge is Eventually Consistent // Devin Stein // #335

    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/YTJoinIn Get 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

    55분
  2. LinkedIn Recommender System Predictive ML vs LLMs

    8월 12일

    LinkedIn Recommender System Predictive ML vs LLMs

    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

    48분
  3. GPU Considerations, Labeling Privacy, Rapid Fine Tuning, and the Role of Private Eval Pipelines to Benchmark New Models

    8월 9일

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

    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/]

    56분
  4. The Hidden Bottlenecks Slowing Down AI Agents

    8월 5일

    The Hidden Bottlenecks Slowing Down AI Agents

    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/]

    48분
  5. 9 Commandments for Building AI Agents

    8월 1일

    9 Commandments for Building AI Agents

    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/]

    1시간 21분
  6. Enterprise AI Adoption Challenges

    7월 29일

    Enterprise AI Adoption Challenges

    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/]

    1시간 5분
  7. Real-time Feature Generation at Lyft // Rakesh Kumar // #334

    7월 25일

    Real-time Feature Generation at Lyft // Rakesh Kumar // #334

    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

    58분
  8. AI Agent Development Tradeoffs You NEED to Know

    7월 22일

    AI Agent Development Tradeoffs You NEED to Know

    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

    57분
모두 보기(457개)

4.7
최고 5점
21개의 평가

소개

Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)

정보

  • 제작진
    Demetrios
  • 방송 연도
    2020년 - 2025년
  • 에피소드
    457
  • 등급
    전체 연령 사용가
  • 저작권
    © Demetrios
  • 웹사이트 보기
    MLOps.community

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