CI/CD for Machine Learning focuses on automating the end-to-end lifecycle of machine learning models, from development and testing to deployment and monitoring. It enables organizations to deliver ML models faster, with improved reliability and consistency in production environments. This training explains core concepts such as continuous integration, continuous delivery, model versioning, and automated testing for ML pipelines. It also covers data validation, experiment tracking, containerization, and deployment automation using modern DevOps tools. You will learn how CI/CD practices help streamline collaboration between data scientists and engineering teams while ensuring scalable ML systems. The course also highlights best practices for building robust, repeatable, and production-ready machine learning workflows.
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