We're All Finetuning Incorrectly // MLOps Podcast #304 with Tanmay Chopra, Founder & CEO of Emissary.

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// Abstract

Finetuning is dead. Finetuning is only for style. We've all heard these claims. But the truth is, we feel this way because all we've been doing is extended pretraining. I'm excited to chat about what real finetuning looks like - modifying output heads, loss functions, and model layers, and its implications on quality and latency. Happy to dive deeper into how DeepSeek leveraged this real version of finetuning through GRPO and how this is nothing more than a rediscovery of our old finetuning ways. I'm sure we'll naturally also dive into when developing and deploying your specialized models makes sense and the challenges you face when doing so.

// Bio

Tanmay is a machine learning engineer at Neeva, where he's currently engaged in reimagining the search experience through AI - wrangling with LLMs and building cold-start recommendation systems. Previously, Tanmay worked on TikTok's Global Trust&Safety Algorithms team - spearheading the development of AI technologies to counter violent extremism and graphic violence on the platform across 160+ countries. Tanmay has a bachelor's and master's in Computer Science from Columbia University, with a specialization in machine learning.

Tanmay is deeply passionate about communicating science and technology to those outside its realm. He's previously written about LLMs for TechCrunch, held workshops across India on the art of science communication for high school and college students, and is the author of Black Holes, Big Bang and a Load of Salt - a labor of love that elucidated the oft-overlooked contributions of Indian scientists to modern science and helped everyday people understand some of the most complex scientific developments of the past century without breaking into a sweat!

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Timestamps:

[00:00] Tanmay's preferred coffee

[00:17] Takeaways

[00:55] LLM Potential vs Reality

[06:41] Prompting and Workflow Challenges

[13:39] LLM Fine-Tuning vs Prompt Engineering

[16:53] Foundational Models vs ML

[23:32] Vertical vs Horizontal Workflows

[28:25] AI CoE Concerns

[32:09] 500 Examples vs API

[36:38] LLM as DAG Node

[39:26] Success with AI Tools

[43:45] AI for regional ads

[48:13] AI Systems and Infrastructure

[51:13] Prompt Experimentation and Evaluation

[56:38] Python vs IML Tools

[59:32] Wrap up