Every ML project starts in a notebook, and nearly every production incident traces back to one that never left. MLOps begins with understanding why the exploratory workflow that makes research fast makes production fragile, and what structure replaces it.
The Hidden State Problem
Notebooks execute cells in whatever order you clicked them, accumulating invisible state. A model trained this way may depend on a cell you deleted, a variable you overwrote, or data loaded from a path that exists only on your laptop. Rerun the notebook top to bottom and you frequently get different results — which means nobody can reproduce the model, including you next month.
From Cells to Pipeline Steps
The production alternative decomposes work into explicit steps — ingest, validate, transform, train, evaluate, register — each a function or script with declared inputs and outputs. Steps read from and write to defined storage locations rather than shared memory, so any step can be rerun, tested, and swapped independently. This decomposition is the raw material every orchestrator in this module consumes.
Reproducibility as a Requirement
Production ML demands that anyone can rebuild any model from scratch: same code version, same data snapshot, same configuration, same random seeds. That standard turns pinned dependencies, versioned data, and parameterized scripts from bureaucratic chores into the actual definition of done for an ML task.
Action Step
Take one of your existing notebooks and refactor it into three or four Python functions with explicit inputs and outputs, orchestrated by a short main script. Run it twice from a clean environment and confirm the outputs are identical.
Educational content only. The AI/ML tooling landscape changes quickly — verify current library versions, APIs, and best practices in official documentation. This course does not certify anyone.