Turn notebook steps into contracts
Separate extraction, validation, transformation, training, evaluation, and release into explicit steps. Define the inputs, outputs, and failure behavior of each step. Version datasets and artifacts so a completed run can be traced back to its inputs. Make retries safe by avoiding duplicate writes and ambiguous output paths.
Draw a pipeline for a churn model. Label the artifacts passed between steps and indicate where a failure should stop the run.
Gate model promotion
Successful training is not enough to release a model. Check schema, data quality, evaluation thresholds, slice performance, and compatibility with serving. Use the same transformation definitions across training and serving. Compare a candidate with the deployed baseline before promotion and retain the evidence for a release decision.
Define three release gates, including one for a minority customer segment. Explain what should happen if the overall score improves but that segment becomes worse.
Retrain for a reason
A schedule is simple, but drift or new labels may be better triggers for some systems. Define what evidence initiates retraining, how the candidate is evaluated, and who or what approves release. A detected distribution change is a signal to investigate; it does not prove that deploying a newly trained model will help.
Write a retraining policy that separates a drift alert, training execution, and model promotion.
A retrained model completes successfully but fails a slice-level quality threshold. What should the pipeline do?
Keep exploring
Use the official certification page and its linked resources to validate your coverage. For guided labs, explore the Google Skills learning path. Revisit the practical exercise and explain your decision in your own words before moving on.