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The practice of applying DevOps principles to Machine Learning systems, encompassing the entire process of building, deploying, and maintaining machine learning models. It involves managing data, model training, validation, deployment, and monitoring in a structured and automated way to ensure the smooth and efficient operation of ML systems throughout their lifecycle. This includes version control, testing, and continuous integration/deployment (CI/CD) for machine learning models.