MLOps

MLOps is the discipline of operating machine learning systems in production: versioning, deployment, monitoring, retraining, and governance.

What is MLOps?

MLOps applies software engineering and DevOps practices to ML. Core capabilities include data and model versioning, reproducible training, automated evaluation, deployment with canary or shadow patterns, drift monitoring, and rollback. Mature MLOps platforms also track lineage, feature stores, and approvals. The goal is the same as DevOps: make change safe, fast, and auditable.

How does MLOps apply to enterprise AI?

Enterprises with multiple ML models in production need MLOps to avoid silent degradation, regulatory non-compliance, and accidental data leakage. Under the EU AI Act, MLOps tooling provides much of the post-market monitoring evidence regulators expect.