MLOps

MLOps unifies ML development (Dev) with system operations (Ops), creating automated, production-grade pipelines for machine learning models.

MLOps (Machine Learning Operations) is the engineering discipline that applies DevOps principles to the ML lifecycle: it’s how we move models from experiment to enterprise scale. We implement CI/CD/CT (Continuous Integration, Delivery, and Training) to automate the entire workflow, from data validation to model deployment and continuous monitoring (CM). This approach ensures models—like a fraud detection classifier or a recommendation engine—are reproducible, versioned, and automatically retrained when performance degrades (model drift). Using platforms like Kubeflow or AWS SageMaker, MLOps reduces deployment time from months to minutes, minimizing technical debt and maintaining high-velocity production reliability.

https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning