With re:Invent 2019 behind me, I have a fairly light blogging load for the rest of the month. I do, however, have a collection of late-breaking news and links that I want to share while they are still hot out of the oven!
AWS Online Tech Talks for December – We have 18 tech talks scheduled for the remainder of the month. You can lean about Running Kubernetes on AWS Fargate, What’s New with AWS IoT, Transforming Healthcare with AI, and much more!
AWS Outposts: Ordering and Installation Overview – This video walks you through the process of ordering and installing an Outposts rack. You will learn about the physical, electrical, and network requirements, and you will get to see an actual install first-hand.
NFL Digital Athlete – We have partnered with the NFL to use data and analytics to co-develop the Digital Athlete, a platform that aims to improve player safety & treatment, and to predict & prevent injury. Watch the video in this tweet to learn more:
AWS JPL Open Source Rover Challenge – Build and train a reinforcement learning (RL) model on AWS to autonomously drive JPL’s Open-Source Rover between given locations in a simulated Mars environment with the least amount of energy consumption and risk of damage. To learn more, visit the web site or watch the Launchpad Video.
Map for Machine Learning on AWS – My colleague Julien Simon created an awesome map that categories all of the ML and AI services. The map covers applied ML, SageMaker’s built-in environments, ML for text, ML for any data, ML for speech, ML for images & video, fraud detection, personalization & recommendation, and time series. The linked article contains a scaled-down version of the image; the original version is best!
Verified Author Badges for Serverless App Repository – The authors of applications in the Serverless Application Repository can now apply for a Verified Author badge that will appear next to the author’s name on the application card and the detail page.
Machine Learning Embark – This new program is designed to help companies transform their development teams into machine learning practitioners. It is based on our own internal experience, and will help to address and overcome common challenges in the machine learning journey. Read the blog post to learn more.