STAGE 1: Define the prediction service
Concept: An ML system includes data, model and operating constraints.
Task: Specify input schema, output labels, prediction timing and fallback behavior.
Acceptance: The fallback remains usable when confidence is low.

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STAGE 2: Track the experiment
Concept: Reproducibility needs data and configuration versions.
Task: Record dataset checksum, split rule, baseline, model settings and evaluation command.
Acceptance: Two runs can be compared without relying on memory.

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STAGE 3: Set release gates
Concept: A deployment decision requires more than a higher aggregate score.
Task: Define minimum precision/recall, latency and subgroup review requirements.
Acceptance: Thresholds are proposed for this case, not universal industry rules.

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STAGE 4: Plan monitoring
Concept: Drift is a signal to investigate, not proof of model failure.
Task: Draft data-quality, latency and delayed-outcome monitoring with rollback ownership.
Acceptance: Separate data drift, prediction drift and measured outcome degradation.

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