MLOps.community Podcast Por Demetrios capa

MLOps.community

MLOps.community

De: Demetrios
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 Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)Demetrios
Episódios
  • Does AgenticRAG Really Work?

    Does AgenticRAG Really Work?
    Dec 12 2025
    Satish Bhambri is a Sr Data Scientist at Walmart Labs, working on large-scale recommendation systems and conversational AI, including RAG-powered GroceryBot agents, vector-search personalization, and transformer-based ad relevance models.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractThe MLOps Community Podcast features Satish Bhambri, Senior Data Scientist with the Personalization and Ranking team at Walmart Labs and one of the emerging leaders in applied AI, in its newest episode. Satish has quietly built one of the most diverse and impactful AI portfolios in his field, spanning quantum computing, deep learning, astrophysics, computer vision, NLP, fraud detection, and enterprise-scale recommendation systems. Bhambri's nearly a decade of research across deep learning, astrophysics, quantum computing, NLP, and computer vision culminated in over 10 peer-reviewed publications released in 2025 through IEEE and Springer, and his early papers are indexed by NASA ADS and Harvard SAO, marking the start of his long-term research arc. He also holds a patent for an AI-powered smart grid optimization framework that integrates deep learning, real-time IoT sensing, and adaptive control algorithms to improve grid stability and efficiency, a demonstration of his original, high-impact contributions to intelligent infrastructure. Bhambri leads personalization and ranking initiatives at Walmart Labs, where his AI systems serve more than (5% of the world’s population) 531 million users every month, roughly based on traffic data. His work with Transformers, Vision-Language Models, RAG and agentic-RAG systems, and GPU-accelerated pipelines has driven significant improvements in scale and performance, including increases in ad engagement, faster compute by and improved recommendation diversity.Satish is a Distinguished Fellow & Assessor at the Soft Computing Research Society (SCRS), a reviewer for IEEE and Springer, and has served as a judge and program evaluator for several elite platforms. He was invited to the NeurIPS Program Judge Committee, the most prestigious AI conference in the world, and to evaluate innovations for DeepInvent AI, where he reviews high-impact research and commercialization efforts. He has also judged Y Combinator Startup Hackathons, evaluating pitches for an accelerator that produced companies like Airbnb, Stripe, Coinbase, Instacart, and Reddit.Before Walmart, Satish built supply-chain intelligence systems at BlueYonder that reduced ETA errors and saved retailers millions while also bringing containers to the production pipeline. Earlier, at ASU’s School of Earth & Space Exploration, he collaborated with astrophysicists on galaxy emission simulations, radio burst detection, and dark matter modeling, including work alongside Dr. Lawrence Krauss, Dr. Karen Olsen, and Dr. Adam Beardsley.On the podcast, Bhambri discusses the evolution of deep learning architectures from RNNs and CNNs to transformers and agentic RAG systems, the design of production-grade AI architectures with examples, and his long-term vision for intelligent systems that bridge research and real-world impact. and the engineering principles behind building production-grade AI at a global scale.// Related LinksPapers: https://scholar.google.com/citations?user=2cpV5GUAAAAJ&hl=enPatent: https://search.ipindia.gov.in/DesignApplicationStatus ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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
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    1 hora e 2 minutos
  • How Sierra AI Does Context Engineering

    How Sierra AI Does Context Engineering
    Dec 10 2025

    Zack Reneau-Wedeen is the Head of Product at Sierra, leading the development of enterprise-ready AI agents — from Agent Studio 2.0 to the Agent Data Platform — with a focus on richer workflows, persistent memory, and high-quality voice interactions.


    How Sierra Does Context Engineering, Zack Reneau-Wedeen // MLOps Podcast #350


    Join the Community:

    https://go.mlops.community/YTJoinIn

    Get the newsletter: https://go.mlops.community/YTNewsletter


    // Abstract

    Sierra’s Zack Reneau-Wedeen claims we’re building AI all wrong and that “context engineering,” not bigger models, is where the real breakthroughs will come from. In this episode, he and Demetrios Brinkmann unpack why AI behaves more like a moody coworker than traditional software, why testing it with real-world chaos (noise, accents, abuse, even bad mics) matters, and how Sierra’s simulations and model “constellations” aim to fix the industry’s reliability problems. They even argue that decision trees are dead, replaced by goals, guardrails, and speculative execution tricks that make voice AI actually usable. Plus: how Sierra trains grads to become product-engineering hybrids, and why obsessing over customers might be the only way AI agents stop disappointing everyone.


    // Related Links

    Website: https://www.zackrw.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 Zack on LinkedIn: /zackrw/


    Timestamps:

    [00:00] Electron cloud vs energy levels

    [03:47] Simulation vs red teaming

    [06:51] Access control in models

    [10:12] Voice vs text simulations

    [13:12] Speaker-adaptive turn-taking

    [18:26] Accents and model behavior

    [23:52] Outcome-based pricing risks

    [31:40] AI cross-pollination strategies

    [41:26] Ensemble of models explanation

    [46:47] Real-time agents vs decision trees

    [50:15] Code and no-code mix

    [54:04] Goals and guardrails explained

    [56:23] Wrap up

    [57:31] APX program!
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    1 hora e 4 minutos
  • Overcoming Challenges in AI Agent Deployment: The Sweet Spot for Governance and Security // Spencer Reagan // #349

    Overcoming Challenges in AI Agent Deployment: The Sweet Spot for Governance and Security // Spencer Reagan // #349
    Dec 5 2025

    Spencer Reagan leads R&D at Airia, working on secure AI-agent orchestration, data governance systems, and real-time signal fusion technologies for regulated and defense environments.


    Overcoming Challenges in AI Agent Deployment: The Sweet Spot for Governance and Security // MLOps Podcast #349 with Spencer Reagan, R&D at Airia.


    Join the Community:

    https://go.mlops.community/YTJoinIn

    Get the newsletter: https://go.mlops.community/YTNewsletter


    Shoutout to Airia for powering this MLOps Podcast episode.


    // Abstract

    Spencer Reagan thinks it might be, and he’s not shy about saying so. In this episode, he and Demetrios Brinkmann get real about the messy, over-engineered state of agent systems, why LLMs still struggle in the wild, and how enterprises keep tripping over their own data chaos. They unpack red-teaming, security headaches, and the uncomfortable truth that most “AI platforms” still don’t scale. If you want a sharp, no-fluff take on where agents are actually headed, this one’s worth a listen.


    // Bio

    Passionate about technology, software, and building products that improve people's lives.


    // Related Links

    Website: https://airia.com/

    Machine Learning, AI Agents, and Autonomy // Egor Kraev // MLOps Podcast #282 - https://youtu.be/zte3QDbQSek

    Re-Platforming Your Tech Stack // Michelle Marie Conway & Andrew Baker // MLOps Podcast #281 - https://youtu.be/1ouSuBETkdA


    ~~~~~~~~ ✌️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 Spencer on LinkedIn: /spencerreagan/


    Timestamps:

    [00:00] AI industry future

    [00:55] Use cases in software

    [05:44] LLMs for data normalization

    [11:02] ROI and overengineering

    [15:58] Street width history

    [20:58] High ROI examples

    [25:16] AI building challenges

    [33:37] Budget control challenges

    [39:30] Airia Orchestration platform

    [46:25] Agent evaluation breakdown

    [53:48] Wrap up
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    54 minutos
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