MLOps: AI Deployment Pipeline training course

Canary deployment lab:

routing a configurable percentage of live traffic to a new model version and monitoring quality across the split

Health check and rollback:

defining numeric failure thresholds and testing automatic rollback behaviour in a staging environment

Dependency management:

pinning library versions, building fully reproducible environments, and resolving the conflicts that arise

Model registry setup:

configuring approval gates, promotion stages, and a complete audit trail using MLflow in a team environment

Pipeline monitoring:

alerting on build failures, deployment errors, and latency regressions before they affect production users

Compliance documentation:

producing a structured deployment record and change log suitable for an internal security or audit review