Getting AI Apps Past the Demo // Vaibhav Gupta // #319

Getting AI Apps Past the Demo // Vaibhav Gupta // #319

Author: Demetrios May 30, 2025 Duration: 50:29

Getting AI Apps Past the Demo // MLOps Podcast #319 with Vaibhav Gupta, CEO of BoundaryML.


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// Abstract

It's been two years, and we still seem to see AI disproportionately more in demos than production features. Why? And how can we apply engineering practices we've all learned in the past decades to our advantage here?


// Bio

Vaibhav is one of the creators of BAML and a YC alum. He spent 10 years in AI performance optimization at places like Google, Microsoft, and D.E. Shaw. He loves diving deep and chatting about anything related to Gen AI and Computer Vision!


// Related Links

Website: https://www.boundaryml.com/


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Timestamps:

[00:00] Vaibhav's preferred coffee

[00:38] What is BAML

[03:07] LangChain Overengineering Issues

[06:46] Verifiable English Explained

[11:45] Python AI Integration Challenges

[15:16] Strings as First-Class Code

[21:45] Platform Gap in Development

[30:06] Workflow Efficiency Tools

[33:10] Surprising BAML Insights

[40:43] BAML Cool Projects

[45:54] BAML Developer Conversations

[48:39] 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.
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