In this video
What you'll learn
Score your LLM task for fine-tuning fit
Use a simple rubric to decide whether your workflow is worth a small open-weight fine-tuning test.
Separate fine-tuning from prompting and RAG
Avoid wasted GPU time by learning when to tune behavior, when to retrieve knowledge, and when a better prompt is enough.
Spot small-model opportunities
Identify narrow workflow steps where a fine-tuned smaller model may reduce cost, latency, or dependency.
Why this topic matters
Open-weight models are now capable enough for many narrow workflows, while proprietary LLM costs can become painful at scale. Fine-tuning is more accessible than many teams assume, but only for the right tasks. This lesson gives you a quick diagnostic for deciding whether your workflow is a good candidate for a 200-example open-weight fine-tuning test.
You'll learn from
Daniel Voigt Godoy
Amazon best-selling author, Instructor @ Linux Foundation/Data Science Retreat
Author of PyTorch and LLM books, with 21,000+ books sold and translations in Chinese and Korean. He has taught for the Linux Foundation and Data Science Retreat, and is the author of an edX course, PyTorch and Deep Learning for Decision Makers.
His professional background includes 25+ years of experience working for companies in several industries: banking, government, fintech, retail, mobility, and edutech.
Instructor at
Go deeper with a course
Fine-Tuning Open-Weight LLMs for Engineers
Daniel Voigt Godoy
Amazon best-selling author, Instructor @ Linux Foundation/Data Science Retreat
Keep exploring
