Learn how to use the Amazon SageMaker and Amazon DataZone integration to streamline machine learning (ML) governance across your infrastructure, user permissions and assets. Machine learning (ML) administrators can define infrastructure controls and permissions in DataZone for projects. Using these controls, ML builders can create appropriate SageMaker environments and kick start their development process inside SageMaker Studio. ML builders can search, discover, and subscribe to data and ML assets in their enterprise business catalog. They can also consume these assets in ML tasks such as data preparation, model training, and feature engineering via tools like JupyterLab and SageMaker Canvas. Upon completing the ML tasks, ML builders can publish the resulting assets such as models and feature groups to the enterprise business catalog for enterprise governance and discoverability. Follow AWS OnAir: LinkedIn: bit.ly/AWSOnAir-LinkedIn Twitch: bit.ly/Twitch-AWS-OnAir ...