Audit Logging for ML Workflows focuses on tracking and recording all activities across machine learning pipelines to ensure transparency, security, and compliance. It enables organizations to monitor data usage, model changes, and system events throughout the ML lifecycle. This training explains core concepts such as logging frameworks, event tracking, pipeline observability, and metadata management. It also covers model versioning logs, data lineage, access tracking, and compliance reporting. You will learn how audit logs help detect issues, support governance, and improve accountability in ML systems. The course also highlights best practices for building traceable, secure, and production-ready machine learning workflows.
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