Real time features, AI search, Agentic similarities

Real time features, AI search, Agentic similarities

Author: Demetrios December 28, 2025 Duration: 29:27

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 #354


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And huge thanks to Chroma for hosting us in their recording studio

// Abstract

Feature 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 Links

Website: https://zipline.ai/


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Connect 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


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 100

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