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.