A set of practices and tools for standardising and streamlining the end-to-end machine learning lifecycle, including development, deployment, monitoring, and governance. MLOps extends DevOps principles to address the unique challenges of machine learning systems.

MLOps practices help organisations reliably build, deploy, and maintain AI systems at scale. Unlike traditional software, AI systems depend heavily on data and exhibit complex behaviours that can change over time, requiring specialised approaches to testing, versioning, monitoring, and updating. MLOps frameworks typically include infrastructure for model training, version control for both code and data, automated testing, deployment mechanisms, performance monitoring, and governance controls.

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An enterprise implementing an MLOps platform that tracks model versions, training datasets, and performance metrics while automating model testing, deployment, and monitoring across development, staging, and production environments.