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Machine Learning Operations (MLOps) is a practice that unifies ML model development, deployment and maintenance across teams. It combines data engineering, CI/CD, monitoring and governance to productionize models reliably. MLOps defines roles, pipelines and automation to ensure reproducibility, scalability and continuous improvement in ML systems.
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MLOps combines machine learning with software and data operations so models can be developed, delivered, monitored, and updated reproducibly.
MLOps grew from the need to run experimental machine-learning models reliably as live services. By combining DevOps automation with data and model versioning and continuous monitoring, it became a distinct operations discipline; Google Cloud describes such delivery pipelines, and Kubeflow provides open-source tooling for them.
Imagine a production line whose workpiece is a model: data is prepared, the model is tested, deployed, and observed in real use. When data or behavior changes, the controlled flow runs again.
Code and data and model versions are recorded so training can be repeated and traced.
Validated models are moved into a usage environment automatically or under control.
Quality, drift, cost, and availability are observed after deployment.
MLOps reduces the risk that a model works in an experiment but is not traceable or maintainable in production.
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