Today we continue our coverage of the AWS ML Summit joined by Chris Fregly, a principal developer advocate at AWS, and Antje Barth, a senior developer advocate at AWS.
In our conversation with Chris and Antje, we explore their roles as community builders prior to, and since, joining AWS, as well as their recently released book Data Science on AWS. In the book, Chris and Antje demonstrate how to reduce cost and improve performance while successfully building and deploying data science projects.
We also discuss the release of their new Practical Data Science Specialization on Coursera, managing the complexity that comes with building real-world projects, and some of their favorite sessions from the recent ML Summit (which you can catch the videos for here).
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 twimlai.com/go/awsml.
And also be sure to check out twimlai.com/go/sagemaker to sign up for SageMaker Studio Lab.