MLOps is the discipline of deploying and maintaining machine learning models in production reliably and efficiently. An MLOps pipeline encompasses model versioning, automated testing, deployment automation, performance monitoring, and rollback capabilities. In governed AI systems, MLOps pipelines also enforce compliance gates at every stage.
Key Concepts
01Model versioning — every model deployment tagged with version, training data hash, and performance benchmarks
02Automated testing — validation suites run before any model promotion to production
03Canary deployments — gradual rollout with automatic rollback on performance degradation
04Drift detection — continuous monitoring for data drift and model performance degradation
05Governance gates — compliance checks embedded in the deployment pipeline, not bolted on after