MLOps, or Machine Learning Operations, combines DevOps practices with machine learning workflows to streamline the deployment, monitoring, and management of machine learning models in production environments. It emphasizes automation, collaboration, and reproducibility throughout the ML lifecycle.
CI/CD for ML involves automating the processes of building, testing, and deploying machine learning models. It ensures that changes to models are rapidly integrated and deployed into production, maintaining consistency and reliability.
Monitoring and governance in MLOps involve continuously monitoring deployed models' performance and ensuring compliance with regulatory standards. It includes detecting drift, managing data quality, and maintaining model fairness and transparency.
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