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MLOps , short for Machine Learning Operations , is a critical function in the field of Machine Learning engineering . It focuses on streamlining the process of taking machine learning models from development to production and then maintaining and monitoring them. MLOps involves collaboration among data scientists, DevOps engineers, and IT professionals 1 2 . Here are some key points about MLOps: Purpose of MLOps : Streamlining Production : MLOps ensures a smooth transition of machine learning models from research environments to production systems. Continuous Improvement : It facilitates experimentation, iteration, and continuous enhancement of the machine learning lifecycle. Collaboration : MLOps bridges the gap between data engineering, data science, and ML engineering teams. Benefits of MLOps : Efficiency : Faster model development, higher-quality ML models, and quicker deployment. Scalability : Management of thousands of models with continuous integration, delivery, and deployment....