What It Is
MLOps covers data versioning, reproducible training, experiment tracking, testing, deployment, monitoring, and retraining, making machine learning repeatable and safe. The goal is to make machine learning repeatable, scalable, and reliable rather than a one-off project owned by a single data scientist.
Key Points
- CI/CD for models: automated testing and release.
- Model registry: tracks versions and approvals.
- Drift monitoring: detects degrading performance.
- Governance: lineage, audit trails, and documentation.
Why It Matters
Models degrade as real-world data changes, so production monitoring is as important as initial accuracy. A mature setup includes clear approval and rollback procedures, so teams can retrain and redeploy models safely as data changes. Strong MLOps turns promising experiments into dependable products.
How ClearLeaff Applies It
We build stacks with Kubeflow and MLflow on cloud-agnostic Kubernetes, supporting sub-100ms inference at scale and operating models with the discipline of any critical software.