Ml Feature Engineering

Trigger Boundary

• •Use when ML data, model, training, evaluation, or serving choices are being made.
• •Do not use for generic API lifecycle governance; use api-*.
• •Do not use for non-ML database administration concerns.

Goal

Produce reliable ML lifecycle decisions from data to production monitoring.

Inputs

• •Change scope and risk profile
• •Domain evidence for feature generation, feature store consistency, and online-offline parity
• •Operational, compliance, and rollout constraints

Outputs

• •Feature specification catalog with lineage
• •Decision log for feature generation, feature store consistency, and online-offline parity
• •Verification checklist with measurable pass-fail criteria

Workflow

1. •Clarify outcomes and hard constraints for feature generation, feature store consistency, and online-offline parity.
2. •Produce options and select an approach for feature generation, feature store consistency, and online-offline parity.
3. •Evaluate trade-offs across security, performance, operability, and maintainability.
4. •Verify decisions using feature drift and leakage checks across training and serving.
5. •Publish decisions, residual risks, and accountable follow-up actions.

Quality Gates

• •Scope and assumptions for feature generation, feature store consistency, and online-offline parity are explicit and reviewable.
• •Decision rationale is backed by evidence instead of preference.
• •Rollout and rollback criteria are defined when production impact exists.
• •Residual risks have owners, due dates, and verification steps.

Failure Handling

• •Stop when feature definitions are inconsistent between training and serving.
• •Escalate when accepted risk exceeds team policy thresholds.