Today we’re joined by Bill Vass, a VP of engineering at Amazon Web Services. Bill spoke at the most recent AWS re:MARS conference, where he delivered an engineering Keynote focused on some recent updates to Amazon sagemaker, including its support for synthetic data generation. In our conversation, we discuss all things synthetic data, including the importance of data quality when creating synthetic data, and some of the use cases that this data is being created for, including warehouses and in the case of one of their more recent acquisitions, iRobot, synthetic house generation. We also explore Astro, the household robot for home monitoring, including the types of models running it, is running, what type of on-device sensor suite it has, and the relationship between the robot and the cloud, and the role of simulation.
AWS offers a broad array of services and infrastructure at all three layers of the machine learning technology stack. More than 100,000 customers trust AWS for Machine Learning, and the company aims to put ML in the hands of every developer, data scientist, and enthusiast with newly announced offerings like Amazon CodeWhisperer, a new ML-powered pair programmer that helps developers improve productivity by reducing the time to build software applications significantly.
To learn more about AWS machine learning services, and how they’re helping customers accelerate their machine learning journeys, visit stage.twimlai.net/go/awsml.
And also be sure to check out stage.twimlai.net/go/sagemaker to sign up for SageMaker Studio Lab.