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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.
You know AWS as a cloud computing technology leader, but did you realize the company offers a broad array of services and infrastructure at all three layers of the machine learning technology stack? AWS has been focused on making ML accessible to customers of all sizes and across industries, and over 100,000 of them trust AWS for machine learning and artificial intelligence services. AWS is constantly innovating across all areas of ML including infrastructure, tools on Amazon SageMaker, and AI services, such as Amazon CodeWhisperer, an AI-powered code companion that improves developer productivity by generating code recommendations based on the code and comments in an IDE. AWS also created purpose-built ML accelerators for the training (AWS Trainium) and inference (AWS Inferentia) of large language and vision models on AWS.
To learn more about AWS ML and AI services, and how they’re helping customers accelerate their machine learning journeys, visit twimlai.com/go/awsml.
