What does it take to fine-tune and deploy a customized LLM on state-of-the-art cloud hardware? In this talk, we explore a fully Pythonic solution to this problem, using just a few extra lines on top of ordinary code. No cloud expertise is required to follow along. We’ll start with a simple example and scale things gradually to arrive at a powerful, high-compute workflow that creates a “model inference” service with a custom API—all within the confines of a Jupyter Notebook!
