Improve ML Pipeline

Operation

Implement the requested change and verify its directly affected behavior.

Goal

Correct the selected data, training or inference path while preserving reproducible inputs and compatible model artifacts.

Domain rules

• Fix data/split/feature ownership before tuning model complexity.
• Version code, data snapshot/query, schema, preprocessing, model, threshold, and evaluation result as one release lineage.
• Keep test data untouched by iterative selection and add slice/failure analysis where product risk is concentrated.
• Make backfill/retraining, artifact promotion, compatibility, and rollback explicit.

Validation

• Run data-contract/quality/leakage/split tests and reproduce the selected training/evaluation path where feasible.
• Compare against baseline with uncertainty and named slices; verify artifact load and preprocessing compatibility.
• List unavailable private data, accelerator, long-running training, or production-drift gates.

Repair the selected data, preprocessing or artifact compatibility owner before retraining or tuning. Reproduce the failing observation/slice with versioned decision-time inputs and compare the fixed pipeline against the same baseline. Promote an artifact only through the requested, authorized release path after its compatibility/quality gates. Recheck failed slices at their owner, then run Patch Checking on the code/configuration change.