Machine learning (ML) is driving innovation through more accurate predictions, reduced operational overhead, and improved customer experiences. The business results of ML applications have caused a significant demand for data science skills, and it’s hard for organizations to keep pace. In this session, we discuss putting ML within reach of more users – from embedding ML within business applications to enabling line of business analysts supporting finance, operations, marketing, sales, and operations to build their own models directly. We will show ML capabilities embedded in business intelligence services like QuickSight, explain how to you can create ML models using SQL with Redshift ML, and go from CSV files to predictive analytics using no-code service Amazon SageMaker Canvas.