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MLOps describes practices, processes and tools for operationalizing the deployment, monitoring, and governance of machine learning models in production. It combines software engineering, data engineering, and DevOps principles to ensure reproducibility, automation, and continuous improvement. Focus is on end-to-end pipelines, monitoring, and lifecycle manage…
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MLOps combines machine learning, software engineering, and operations so models can be trained, delivered, monitored, and updated reproducibly.
MLOps grew from the experience that machine-learning models depend on data, training runs, and model artifacts in addition to source code, and that their behavior changes in production. Practice therefore combined DevOps ideas with the ML lifecycle; during the 2020s, cloud guidance and platforms such as Kubeflow described this automation as an integrated delivery pipeline.
MLOps is a production line: data is raw material, training is manufacturing, validation is quality control, and monitoring feeds back from real use.
It covers data preparation, training, validation, deployment, monitoring, and retirement.
It is a material change in input data compared with the training or reference distribution.
MLOps makes ML products traceable and operable, joining technical quality with data lineage, model performance, and safe updates.
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