Stage 1 · Lesson 1 of 8
The lifecycle and the tool map
MLOps is the set of practices that keep a model useful after it leaves a notebook. The lifecycle is a loop: collect and validate data, train, evaluate, approve, deploy, serve, monitor, and retrain when the . is the same loop for LLM features, where the moving parts are prompts, eval sets (fixed test cases with expected answers) and rather than trained weights.
Every tool in this roadmap automates one arrow of that loop: DVC for data versions, MLflow for runs and the registry, an for pipelines, a for the API. Learn the job each arrow does before the tool names.
Common trapthinking deployment is the finish line, when most of a model's cost and risk arrives after it goes live.
A question to check yourself, an AI practice session and 2 cheat sheet entries come with the full roadmap. Unlock
Resources for this stage
Start with· follow one of these
- CoursePaidGrokking Python Fundamentals
Design Gurus · Beginner · $50
If your Python stops at notebooks: modules, functions, classes and error handling done properly
- CourseFreeMade With ML
Goku Mohandas · Advanced
Free: the scripting, CLI, testing and documentation lessons turn notebook code into a real project