AI and Data Platforms
ML Platform Operations
Machine learning platforms support data scientists and engineers through repeatable training, evaluation, deployment, monitoring, and governance. They need software delivery discipline plus data and model lifecycle controls.
Platform capabilities
Common capabilities include:
- Experiment tracking.
- Feature and dataset management.
- Training orchestration.
- Model registry.
- Batch and online inference.
- Model monitoring and drift detection.
- Reproducible environments.
- Approval workflows for regulated models.
Model lifecycle
Operational requirements
Production models should define:
- Owner and support path.
- Training data lineage.
- Feature definitions and freshness.
- Model version and artifact location.
- Evaluation metrics and approval criteria.
- Deployment and rollback approach.
- Inference latency, error, and saturation metrics.
- Drift, bias, and quality monitoring where applicable.
Governance
Governance requirements depend on model impact. High-impact models may need explainability, approval workflows, audit trails, human override, retention policy, and periodic review.
Watchouts
- Notebooks are not production pipelines.
- Training reproducibility fails without pinned data and environments.
- Model drift can degrade outcomes without throwing errors.
- Batch inference still needs observability and recovery paths.