Real time features, AI search, Agentic similarities

Zengyi Qin is the Founder of the OpenAGI Foundation, working on computer-use models and open, agent-centric AI infrastructure.Computers that Think and Take Actions for You, Zengy Qin // MLOps Podcast #355Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps Merch: https://shop.mlops.community/// AbstractWhat if the computer itself can think and take actions for you? You just give it a goal, and it performs every click, type, drag, and gets work done across the desktop and web. In this talk, Zengyi reveals the breakthrough technology that his company OpenAGI is developing: AI that can use computers like humans do. He talks about how his team developed the model, why it outperforms similar models from OpenAI and Google, and its wide use cases across different domains. // Related LinksWebsite: https://www.qinzy.tech/~~~~~~~~ ✌️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: /dpbrinkmConnect with Zengyi on LinkedIn: /qinzy/

Varant Zanoyan is the Co-founder & CEO at Zipline AI, working on building a next-generation AI/ML infrastructure platform that streamlines data pipelines, model deployment, observability, and governance to accelerate enterprise AI development. Nikhil Simha Raprolu is the Co-founder & CTO at Zipline AI, focused on architecting and scaling the company’s AI data platform — extending the open-source Chronon engine into a developer-friendly system that simplifies building and operating production AI applications.Real-time features, AI search, Agentic similarities, Varant Zanoyan & Nikhil Simha Raprolu // MLOps Podcast #354Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps Swag/Merch: [https://shop.mlops.community/]And huge thanks to Chroma for hosting us in their recording studio// AbstractFeature stores might be the wrong abstraction. Varant Zanoyan and Nikhil Simha Raprolu explain why Cronon ditched “store-first” thinking and focused on compute, orchestration, and real-time correctness—born at Airbnb, battle-tested with Stripe. If embeddings, agents, and real-time ML feel painful, this episode explains why.// Related LinksWebsite: https://zipline.ai/ ~~~~~~~~ ✌️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: /dpbrinkmConnect with Varant on LinkedIn: /vzanoyan/Connect with Nikhil on LinkedIn: /nikhilsimha/Timestamps:[00:00] Feature Platform Insights[02:00] Zipline and Feature Stores[05:19] Cronon and Zipline Origins[10:49] Feast and Feather Comparison[13:27] Open source challenges[20:52] Zipline and Iceberg Integration [23:54] Airbnb Agent Systems[28:16] Features vs Embeddings[29:07] Wrap up

Alex Salazar is the CEO and Co-Founder of Arcade.dev, working on secure AI agents and real-world automation integrations.Chiara Caratelli is a Data Scientist at Prosus Group, working on AI agents, web automation, and evaluation of robust multimodal models.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: ⁠https://go.mlops.community/gpuguide// AbstractAgents sound smart until millions of users show up. A real talk on tools, UX, and why autonomy is overrated.// BioChiara CaratelliChiara is a Data Scientist at Prosus, where she develops AI-driven solutions with a focus on AI agents, multimodal models, and new user experiences. With a PhD in Computational Science and a background in machine learning engineering and data science, she has worked on deploying AI-powered applications at scale, collaborating with Prosus portfolio companies to drive real-world impact.Beyond her work at Prosus, she enjoys experimenting with generative AI and art. She is also an avid climber and book reader, always eager to explore new ideas and share knowledge with the AI and ML community.Alex SalazarAlex is the CEO and co-founder of Arcade.dev, the unified agent action platform that makes AI agents production-ready. Previously, Salazar co-founded Stormpath, the first authentication API for developers, which was acquired by Okta. At Okta, he led developer products, accounting for 25% of total bookings, and launched a new auth-centric proxy server product that reached $9M in revenue within a year. He also managed Okta's network of over 7,000 auth integrations. Alex holds a computer science degree from Georgia Tech and an MBA from Stanford University.// Related LinksWebsite: https://www.prosus.com/Website: https://www.arcade.dev/~~~~~~~~ ✌️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: /dpbrinkmConnect with Alex on LinkedIn: /alexsalazar/Connect with Chiara on LinkedIn: /chiara-caratelli/Timestamps:[00:00] Intro[00:15] Insights from iFood[06:22] API vs agent intention[09:45] Tool definition clarity[15:37] Preemptive context loading[27:50] Contextualizing agent data[33:27] Prompt bloat in payments[41:33] Agent building evolution[50:09] Agent program scalability[55:29] Why multi-agent is a dead end[56:17] Wrap up

Jonathan Wall is the CEO at Runloop.ai, working on enterprise-grade infrastructure and execution environments for AI coding agents.The Future of AI Agents is Sandboxed // MLOps Podcast #353 with Jonathan Wall, CEO at Runloop.ai.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterShoutout to  @runloop-ai  for powering this MLOps Podcast episode.// AbstractEveryone’s arguing about agents. Jonathan Wall says the real fight is about sandboxes, isolation, and why most “agent platforms” are doing it wrong.// BioJon was the techlead of Google File System, a founding engineer at Google Wallet, and then the founder of Inde, which was acquired by Stripe. He is building Runloop.ai to bridge the production gap for AI Agents by building a one-stop sandbox infrastructure for building, deploying, and refining agents. // Related LinksWebsite: runloop.aiBlogs and content at https://www.runloop.ai/~~~~~~~~ ✌️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: /dpbrinkmConnect with Jon on LinkedIn: /jonathantwall/Timestamps:[00:00] GitHubification of workflows[00:29] Sandbox definitions explained[04:47] Agent setup explanation[08:03] Sandbox vs API agent[13:51] Resource usage in sandbox [22:50] Agent evaluation setup[28:08] Failure cases value[31:06] Sandbox isolation vs multi-tenancy[36:14] Frameworks vs Harnesses[39:02] Langraph vs Harness comparison[43:22] Agent flexibility and verification[52:51] Training data focus[57:10] Wrap up

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 #352Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractFeature 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.// BioSimba Khadder leads Redis Context Engine and Redis Featureform, building both the feature and context layer for production AI agents and ML models. He joined Redis via the acquisition of Featureform, where he was Founder & CEO. At Redis, he continues to lead the feature store product as well as spearhead Context Engine to deliver a unified, navigable interface connecting documents, databases, events, and live APIs for real-time, reliable agent workflows. He also loves to surf, go sailing with his wife, and hang out with his dog Chupacabra.// Related LinksWebsite: featureform.comhttps://marketing.redis.io/blog/real-time-structured-data-for-ai-agents-featureform-is-joining-redis/~~~~~~~~ ✌️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: /dpbrinkmConnect with Simba on LinkedIn: /simba-k/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

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

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 #350Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractSierra’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 LinksWebsite: https://www.zackrw.com/~~~~~~~~ ✌️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: /dpbrinkmConnect 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!

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/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterShoutout to Airia for powering this MLOps Podcast episode.// AbstractSpencer 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.// BioPassionate about technology, software, and building products that improve people's lives.// Related LinksWebsite: https://airia.com/Machine Learning, AI Agents, and Autonomy // Egor Kraev // MLOps Podcast #282 - https://youtu.be/zte3QDbQSekRe-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/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: /dpbrinkmConnect 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

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.Check out all the talks from the conference here: https://go.mlops.community/carzleGet some "I hallucinate more than ChatGPT" t-shirts here: https://go.mlops.community/NL_RY25_Merch

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 ProductionCheck out all the talks from the conference here: ⁠https://go.mlops.community/carzle⁠Get some "I hallucinate more than ChatGPT" t-shirts here: ⁠https://go.mlops.community/NL_RY25_Merch