Episódios

  • Context engineering 2.0, Agents + Structured Data, and the Redis Context Engine
    Dec 16 2025

    Simba Khadder is the founder and CEO of Featureform, now at Redis, working on real-time feature orchestration and building a context engine for AI and agents.


    Context Engineering 2.0, Simba Khadder // MLOps Podcast #352


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    // Abstract

    Feature stores aren’t dead — they were just misunderstood. Simba Khadder argues the real bottleneck in agents isn’t models, it’s context, and why Redis is quietly turning into an AI data platform. Context engineering matters more than clever prompt hacks.


    // Bio

    Simba Khadder is the founder and CEO of Featureform, the virtual feature store that empowers data scientists to define, manage, and serve model features using a Python framework. He began his machine learning career in recommender systems, where he architected a multi-modal personalization engine that enhanced the experiences of hundreds of millions of users. Later, he open-sourced the data platform that powered this model and built a company around it, Featureform. Outside of ML, Simba is a published astrophysicist, an avid surfer, and once ran a marathon in basketball shoes.


    // Related Links

    Website: featureform.comhttps://marketing.redis.io/blog/real-time-structured-data-for-ai-agents-featureform-is-joining-redis/


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    Timestamps:

    [00:00] Context engineering explanation

    [00:25] MLOps and feature stores

    [03:36] Selling a company experience

    [06:34] Redis feature store evolution

    [12:42] Embedding hub

    [20:42] Human vs agent semantics

    [26:41] Enrich MCP data flow

    [29:55] Data understanding and embeddings

    [35:18] Search and context tools

    [39:45] MCP explained without hype

    [45:15] Wrap up

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    46 minutos
  • 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
    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


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    // 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/


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    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
    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.


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    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


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    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
  • Hardening Agents for E-commerce Scale: From RL Alignment to Reliability // Panel 2
    Dec 2 2025

    Thanks to Prosus Group for collaborating on the Agents in Production Virtual Conference 2025.


    Abstract //

    The discussion centers on highly technical yet practical themes, such as the use of advanced post-training techniques like Direct Preference Optimization (DPO) and Parameter-Efficient Fine-Tuning (PEFT) to ensure LLMs maintain stability while specializing for e-commerce domains. We compare the implementation challenges of Computer-Using Agents in automating legacy enterprise systems versus the stability issues faced by conversational agents when inputs become unpredictable in production. We will analyze the role of cloud infrastructure in supporting the continuous, iterative training loops required by Reinforcement Learning-based agents for e-commerce!


    Bio //

    Paul van der Boor (Panel Host) //

    Paul van der Boor is a Senior Director of Data Science at Prosus and a member of its internal AI group.


    Arushi Jain (Panelist) //

    Arushi is a Senior Applied Scientist at Microsoft, working on LLM post-training for Computer-Using Agent (CUA) through Reinforcement Learning. She previously completed Microsoft’s competitive 2-year AI Rotational Program (MAIDAP), building and shipping AI-powered features across four product teams.


    She holds a Master’s in Machine Learning from the University of Michigan, Ann Arbor, and a Dual Degree in Economics from IIT Kanpur. At Michigan, she led the NLG efforts for the Alexa Prize Team, securing a $250K research grant to develop a personalized, active-listening socialbot. Her research spans collaborations with Rutgers School of Information, Virginia Tech’s Economics Department, and UCLA’s Center for Digital Behavior.


    Beyond her technical work, Arushi is a passionate advocate for gender equity in AI. She leads the Women in Data Science (WiDS) Cambridge community, scaling participation in her ML workshops from 25 women in 2020 to 100+ in 2025—empowering women and non-binary technologists through education and mentorship.


    Swati Bhatia //

    Passionate about building and investing in cutting-edge technology to drive positive impact.


    Currently shaping the future of AI/ML at Google Cloud.


    10+ years of global experience across the U.S, EMEA, and India in product, strategy & venture capital (Google, Uber, BCG, Morpheus Ventures).


    Audi Liu //

    I’m passionate about making AI more useful and safe.


    Why? Because AI will be ubiquitous in every workflow, powering our lives just like how electricity revolutionized our society - It’s pivotal we get it right.


    At Inworld AI, we believe all future software will be powered by voice. As a Sr Product Manager at Inworld, I'm focused on building a real-time voice API that empowers developers to create engaging, human-like experiences. Inworld offers state-of-the-art voice AI at a radically accessible price - No. 1 on Hugging Face and Artificial Analysis, instant voice cloning, rich multilingual support, real-time streaming, and emotion plus non-verbal control, all for just $5 per million characters.


    Isabella Piratininga //

    Experienced Product Leader with over 10 years in the tech industry, shaping impactful solutions across micro-mobility, e-commerce, and leading organizations in the new economy, such as OLX, iFood, and now Nubank. I began my journey as a Product Owner during the early days of modern product management, contributing to pivotal moments like scaling startups, mergers of major tech companies, and driving innovation in digital banking.


    My passion lies in solving complex challenges through user-centered product strategies. I believe in creating products that serve as a bridge between user needs and business goals, fostering value and driving growth. At Nubank, I focus on redefining financial experiences and empowering users with accessible and innovative solutions.

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    29 minutos
  • Building Cursor: A Fireside Chat with VP Solutions Ricky Doar
    Nov 27 2025

    Ricky Doar is the VP of Solutions at Cursor, where he leads forward-deployed engineers. A seasoned product and technical leader with over a decade of experience in developer tools and data platforms, Ricky previously served as VP of Field Engineering at Vercel, where he led global technical solutions for the company's next-generation frontend platform.


    Prior to Vercel, Ricky held multiple leadership roles at Segment (acquired by Twilio), including Director of Product Management for Twilio Engage, Group Product Manager for Personas, and RVP of Solutions Engineering for the West and APAC regions. He also worked as a Product Engineer and Senior Sales Engineer at Mixpanel, bringing deep technical expertise to customer-facing roles.


    Thanks to Prosus Group for collaborating on the Agents in Production Virtual Conference 2025.


    In this session, Ricky Doar, VP of Solutions at Cursor, shares actionable insights from leading large-scale AI developer tool implementations at the world’s top enterprises. Drawing on field experience with organizations at the forefront of transformation, Ricky highlights key best practices, observed power-user patterns, and deployment strategies that maximize value and ensure smooth rollout. Learn what distinguishes high-performing teams, how tailored onboarding accelerates adoption, and which support resources matter most for driving enterprise-wide success.


    A Prosus | MLOps Community Production

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    27 minutos
  • Relational Foundation Models: Unlocking the Next Frontier of Enterprise AI // Jure Leskovec // #348
    Nov 25 2025

    Dr. Jure Leskovec is the Chief Scientist at Kumo.AI and a Stanford professor, working on relational foundation models and graph-transformer systems that bring enterprise databases into the foundation-model era.


    Relational Foundation Models: Unlocking the Next Frontier of Enterprise AI // MLOps Podcast #348 with Jure Leskovec, Professor and Chief Scientist, Stanford University and Kumo.AI.


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    // Abstract

    Today’s foundation models excel at text and images—but they miss the relationships that define how the world works. In every enterprise, value emerges from connections: customers to products, suppliers to shipments, molecules to targets. This talk introduces Relational Foundation Models (RFMs)—a new class of models that reason over interactions, not just data points. Drawing on advances in graph neural networks and large-scale ML systems, I’ll show how RFMs capture structure, enable richer reasoning, and deliver measurable business impact. Audience will learn where relational modeling drives the biggest wins, how to build the data backbone for it, and how to operationalize these models responsibly and at scale.


    // Bio

    Jure Leskovec is the co-founder of Kumo.AI, an enterprise AI company pioneering AI foundation models that can reason over structured business data. He is also a Professor of Computer Science at Stanford University and a leading researcher in artificial intelligence, best known for pioneering Graph Neural Networks and creating PyG, the most widely used graph learning toolkit. Previously, Jure served as Chief Scientist at Pinterest and as an investigator at the Chan Zuckerberg BioHub. His research has been widely adopted in industry and government, powering applications at companies such as Meta, Uber, YouTube, Amazon, and more. He has received top awards in AI and data science, including the ACM KDD Innovation Award.


    // Related Links

    Website: https://cs.stanford.edu/people/jure/

    https://www.youtube.com/results?search_query=jure+leskovec

    Please watch Jure's keynote:

    https://www.youtube.com/watch?v=Rcfhh-V7x2U


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    Connect with Jure on LinkedIn: /leskovec

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    49 minutos
  • Context Engineering, Context Rot, & Agentic Search with the CEO of Chroma, Jeff Huber
    Nov 21 2025

    Jeff Huber is the CEO of ​Chroma, working on context engineering and building reliable retrieval infrastructure for AI systems.


    Context Engineering, Context Rot, & Agentic Search with the CEO of Chroma, Jeff Huber // MLOps Podcast #348.


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    // Abstract

    Jeff Huber drops some hard truths about “context rot” — the slow decay of AI memory that’s quietly breaking your favorite models. From retrieval chaos to the hidden limits of context windows, he and Demetrios Brinkmann unpack why most AI systems forget what matters and how Chroma is rethinking the entire retrieval stack. It’s a bold look at whether smarter AI means cleaner context — or just better ways to hide the mess.


    // Bio

    Jeff Huber is the CEO and cofounder of Chroma. Chroma has raised $20M from top investors in Silicon Valley and builds modern search infrastructure for AI.


    // Related Links

    Website: https://www.trychroma.com/


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    Connect with Jeff on LinkedIn: /jeffchuber/


    Timestamps:

    [00:00] AI intelligence context clarity

    [00:37] Context rot explanation

    [03:02] Benchmarking context windows

    [05:09] Breaking down search eras

    [10:50] Agent task memory issues

    [17:21] Semantic search limitations

    [22:54] Context hygiene in AI

    [30:15] Chroma on-device functionality

    [38:23] Vision for precision systems

    [43:07] ML model deployment challenges

    [44:17] Wrap up

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    45 minutos