What is MLOps?
TL;DR
Practices and tools for efficiently managing the development, deployment, and monitoring of machine learning models.
MLOps: Definition & Explanation
MLOps (Machine Learning Operations) is a set of practices and tools for efficiently managing the entire lifecycle of machine learning models — from development to production deployment, operation, and monitoring. Extending DevOps concepts to ML development, it encompasses model versioning, experiment tracking, data pipeline construction, automated model deployment, performance monitoring, and model drift detection. Platforms like MLflow, Kubeflow, Vertex AI, Amazon SageMaker, and Weights & Biases are widely used. For enterprises advancing AI adoption, MLOps is essential for ensuring model reproducibility and maintaining quality.