Streamline Machine Learning Governance with Amazon DataZone and Amazon SageMaker

Discover in this 18-minute technical video how to integrate Amazon SageMaker with Amazon DataZone for comprehensive machine learning governance. Learn to establish infrastructure controls and permissions for ML projects through DataZone, enabling ML builders to create SageMaker environments and initiate development within SageMaker Studio. Explore methods for searching, discovering, and subscribing to enterprise data and ML assets through the business catalog, while leveraging tools like JupyterLab and SageMaker Canvas for data preparation, model training, and feature engineering tasks. Master the process of publishing models and feature groups back to the enterprise business catalog, ensuring proper governance and discoverability across the organization. Gain insights into streamlining ML workflows while maintaining robust governance controls across infrastructure, user permissions, and assets.