Episode 3 of 15
Full video series click https://aka.ms/AI-300onYouTube
This is where experimentation meets accountability. In this section, you’ll learn how to use MLflow inside notebooks to track model training as it happens—capturing parameters, metrics, artifacts, and runs so nothing is lost, forgotten, or overwritten. By bringing visibility and structure to your experiments, MLflow turns ad‑hoc notebook work into a repeatable, auditable process, helping you understand what worked, compare results over time, and confidently move from experimentation to production‑ready models.
Immerse in rich interactive materials with self-directed learning https://aka.ms/AI-300onLearn
00:00 Video Start
00:06 Session Overview
00:45 Track model training in notebooks with MLflow
15:16 Knowledge Check
16:04 Summary
