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MLOps can be seen as the machine learning counterpart to DevOps. While DevOps concentrates on streamlining software development, testing, and deployment.
MLOps introduces essential procedures for training, deploying, and monitoring machine learning models.
It encompasses aspects such as managing data pipelines, ensuring model version control, and promoting continuous learning. In essence, if DevOps is about optimizing code processes, MLOps focuses on the lifecycle of machine learning models.