AI & ML OpsLast reviewed 2026-09-15
MLOps
Operating models in production: versioning, drift, evaluation, and safe rollout.
Overview
Read the full MLOps guidance
MLOps extends delivery discipline to models: training data and code are versioned together, evaluation is automated and continuous (not a notebook run at launch), drift — data and concept — is monitored in production, and rollout follows the same progressive-delivery rules as any other change. A model is a deployment artifact with an owner, an SLO, and a rollback.
OpsRoadmaps assesses MLOps through experiment reproducibility, automated evaluation gates in CI, production drift monitoring, and retraining/rollback procedures that have actually been exercised.
Capabilities
Model Deployment
Versioned, evaluated, progressively rolled-out ML models with rollback.
Architecture patterns
Reference architectures and their trade-offs live in the blueprints library.
Maturity
Maturity for mlops is measured, not guessed — every score traces to your answers. See how maturity is scored.