Fine-tuning large language models (LLMs) is exciting—until your checkpoints hit gigabytes and datasets scale into the billions of tokens. How do you manage memory, scale training across machines, and fine-tune multiple models—without breaking your workflow or your budget?

The need for specialized LLMs is rapidly growing, especially in Africa, where local languages, cultural nuance, and domain-specific data require models that go beyond generic pre-trained baselines.

This session walks through how to scale LLM fine-tuning using Dask with AWS EC2 and ECS. You'll learn how to spin up clusters, orchestrate parallel jobs, and manage massive datasets efficiently using familiar Python tools.

Expect real-world examples of:
Running multiple fine-tuning jobs in parallel
Managing large tokenized datasets on S3
Optimizing compute cost using spot instances

Whether you’re building copilots or custom LLMs, you’ll leave ready to scale from notebook to cloud cluster—without rewriting your codebase.