An ML delivery pipeline should check code, data assumptions and model artifacts. A passing software test alone does not validate predictive quality.
Before you begin
Know Python, tabular data handling and basic statistics. Use fictional or permitted data and identify the prediction or evaluation task.
Work through the idea
Keep an experiment record with the dataset, split, baseline, metric and limitations. Choose information that would genuinely be available at the time of prediction. Compare the method with a simple alternative and inspect mistakes. A learning experiment should not be presented as a validated production decision system.
- Version code and artifact metadata
- Define quality gates
- Plan rollback and monitoring
Practice task
Design a fictional pipeline that rejects an artifact with incompatible feature definitions.
Check your result
Show the checks before promotion and how the previous model would be restored.
A mistake to avoid
Do not overwrite the only working artifact before a new version passes its checks.
Keep a review note
Save the inputs or scenario, your reasoning and the result you checked. Explain one assumption and one case that needs more work. Revise the exercise after feedback and distinguish what you demonstrated from what remains untested.
Official reference for further study
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