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​​Fine-Tuning Large Language Models with Declarative ML Orchestration

Shivay Lamba

from Couchbase (India)

About speaker

Shivay Lamba is a software developer specializing in DevOps, Machine Learning and Full Stack Development.

He is an Open Source Enthusiast and has been part of various programs like Google Code In and Google Summer of Code as a Mentor and is currently a MLH Fellow.

About speakers company

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Abstracts

Large language models like GPT-3 and BERT have revolutionized natural language processing by achieving state-of-the-art performance. However, these models are typically trained by tech giants with massive resources. Smaller organizations struggle to fine-tune these models for their specific needs due to infrastructure challenges.

This talk will demonstrate how open-source ML orchestration tools like Flyte can help overcome these challenges by providing a declarative way to specify the infrastructure required for ML workloads. Flyte's capabilities can streamline ML pipelines, reduce costs, and make fine-tuning of large language models accessible to a wider audience.

Specifically, attendees will learn:

- How large language models work and their potential applications
- The infrastructure requirements and challenges for fine-tuning these models
- How Flyte's declarative specification and abstractions can automate and simplify infrastructure setup
- How to leverage Flyte to specify ML workflows for fine-tuning large language models
- How Flyte can reduce infrastructure costs and optimize resource usage

By the end of the talk, attendees will understand how open-source ML orchestration tooling can unlock the full potential of large language models by making their fine-tuning easier and more accessible, even with limited resources. This will enable a larger community of researchers and practitioners to leverage and train large language models for their specific use cases.

The talk was accepted to the conference program