Tricks to Fine Tuning // Prithviraj Ammanabrolu // #318

Tricks to Fine Tuning // Prithviraj Ammanabrolu // #318

Author: Demetrios June 11, 2025 Duration: 54:01

Tricks to Fine Tuning // MLOps Podcast #318 with Prithviraj Ammanabrolu, Research Scientist at Databricks.


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// Abstract

Prithviraj Ammanabrolu drops by to break down Tao fine-tuning—a clever way to train models without labeled data. Using reinforcement learning and synthetic data, Tao teaches models to evaluate and improve themselves. Raj explains how this works, where it shines (think small models punching above their weight), and why it could be a game-changer for efficient deployment.


// Bio

Raj is an Assistant Professor of Computer Science at the University of California, San Diego, leading the PEARLS Lab in the Department of Computer Science and Engineering (CSE). He is also a Research Scientist at Mosaic AI, Databricks, where his team is actively recruiting research scientists and engineers with expertise in reinforcement learning and distributed systems.


Previously, he was part of the Mosaic team at the Allen Institute for AI. He earned his PhD in Computer Science from the School of Interactive Computing at Georgia Tech, advised by Professor Mark Riedl in the Entertainment Intelligence Lab.


// Related Links

Website: https://www.databricks.com/


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Timestamps:

[00:00] Raj's preferred coffee

[00:36] Takeaways

[01:02] Tao Naming Decision

[04:19] No Labels Machine Learning

[08:09] Tao and TAO breakdown

[13:20] Reward Model Fine-Tuning

[18:15] Training vs Inference Compute

[22:32] Retraining and Model Drift

[29:06] Prompt Tuning vs Fine-Tuning

[34:32] Small Model Optimization Strategies

[37:10] Small Model Potential

[43:08] Fine-tuning Model Differences

[46:02] Mistral Model Freedom

[53:46] 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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