Free preview lesson · MLOps: Machine Learning in Production · Module 1: ML pipelines
Orchestrating training
A model that cannot be reproduced cannot be trusted. Experiment tracking records what produced each result.
What to record
Code version, data version, parameters, metrics and the resulting artefact.
Why it matters
When performance drops you can compare against a known good run.
Start small
Even a shared spreadsheet beats nothing; then move to a tracking tool.
Common pitfall
Overwriting model files without recording what changed.
Action step
List what you would need to rebuild your last model exactly.
This is one lesson from MLOps: Machine Learning in Production.
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