End-to-end data and machine learning governance on AWS

As the amount of data rapidly expands, our customers want an end-to-end governance strategy that makes it easier to collaborate and share their data while maintaining data quality and security. But creating the right governance controls can be complex and time consuming. It’s difficult to figure out who should have access to what data across your entire organization. That’s why AWS has invested in tools to remove the heavy lifting. In this session we will review how the AWS data and machine learning (ML) services come together to create a modern data governance strategy, with services like AWS Lake Formation to help you govern and audit your data lake on S3, and new governance capabilities for your data warehouse in AWS with Amazon Redshift Data Sharing. We will also explore new ML governance tools for Amazon SageMaker to improve governance of your ML projects with simplified access control and enhanced transparency across your ML model’s lifecycle. And last, we will talk about Amazon DataZone that helps catalog, discover, analyze, share, visualize and govern data across the organization.