Managing Model Lifecycle with MLOps focuses on overseeing the complete journey of machine learning models from development and deployment to monitoring and retraining. It enables organizations to maintain reliable, scalable, and continuously improving ML systems in production environments. This training explains core concepts such as model versioning, experiment tracking, CI/CD pipelines, and workflow automation. It also covers deployment strategies, monitoring, drift detection, retraining workflows, and governance practices. You will learn how enterprises use MLOps to streamline collaboration between data science and engineering teams while ensuring model performance and consistency. The course also highlights best practices for building efficient, traceable, and production-ready machine learning lifecycle management systems.
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