Data Preparation and Fine Tuning for Small language Models with AWS

Not every job needs a large frontier model. A small model, fine-tuned on your own data, can outperform larger ones on specific tasks at a fraction of the cost and latency, whilst helping you to retain the edge your competitors can’t copy – your data. ​This workshop will take you through implementing an end-to-end pipeline for fine-tuning a small LM (Llama 3.2 3B) to generate SQL queries from natural language using Amazon SageMaker’s serverless training and evaluation APIs. You’ll learn how to extract real user queries from an RDS PostgreSQL instance, clean and format them into training data; and then use the Notebook to orchestrates dataset registration, LoRA-based SFT training, and custom Lambda-based evaluation with MLflow experiment tracking. Bring your laptop, and you’ll leave with a repeatable pipeline you can point at your own use case. 12.00pm Lunch & Welcome 12.30-12.40: Kick off and Overview 12.40-2.20: Workshop Part 1 2.20-2.30: Break 2.30-4.15: Workshop Part 2 4.15-4.30: Wrap up Please note this event is targeted at startup builders and developers who already have some background knowledge of working with AI models. Attendance size will be strictly capped to ensure a quality experience for those who join.