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MLOps

Definition The discipline of applying DevOps principles — continuous integration, deployment, monitoring, and automation — to the machine learning lifecycle. MLOps bridges the gap between experimental data science and reliable production AI systems.

In Depth

MLOps covers the full pipeline: data versioning, experiment tracking, model registry, CI/CD for training pipelines, A/B testing, production monitoring for drift and performance degradation, and rollback. Platforms like MLflow, Kubeflow, and cloud-native services (SageMaker, Vertex AI) provide tooling. Without MLOps discipline, models degrade silently in production as real-world data distributions shift.

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