mle-workflow

Reproducible training requires packaging model configs, baselines, metrics, and artifact tracking so training runs can be reliably rerun and reviewed. This ensures consistent evaluation before promoting any ML system to production.

Data contracts define ML system inputs, outputs, label timing, freshness rules, and leakage checks before implementation. They are needed when transitioning ad hoc model work into a production-ready machine learning workflow.

Define deployment gates by setting explicit serving schema validations and baseline promotion criteria. Establish rollback paths by preparing operational monitoring signals and drift-aware alerts to safely operate classification systems.

Yes, you can review a forecasting model by checking its offline metrics, inference path, and monitoring plan. The workflow evaluates data contracts, reproducible training, and deployment readiness to determine if it is safe to ship.

Drift-aware operational monitoring tracks production ML system signals to detect data or prediction shifts over time. It protects systems by triggering predefined rollback paths when serving schemas or model performance degrade.

Prevent data leakage in ranking systems by enforcing explicit prediction and data contracts before implementation. These contracts define label timing and freshness rules to ensure no future data leaks into training or evaluation gates.