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MLOps (Machine Learning Operations) is a set of procedures that aims to streamline and integrate the end-to-end machine learning lifecycle, from development to deployment and monitoring through the integration of principles from DevOps and data engineering. It involves automating workflows, version control for models and data, continuous integration and deployment (CI/CD), and monitoring model performance. MLOps helps organisations deploying and handling machine learning models at scale, ensuring reproducibility, scalability, and reliability throughout the entire machine learning pipeline.