This month in AWS Machine Learning: October edition

Every day there is something new going on in the world of AWS Machine Learning—from launches to new to use cases to interactive trainings. We’re packaging some of the not-to-miss information from the ML Blog and beyond for easy perusing each month. Check back at the end of each month for the latest roundup.

Launches

This month we announced price drops for Amazon SageMaker, improvements to Amazon Personalize, and personal protective equipment (PPE) detection for Amazon Rekognition.

AWS has reduced the price of Amazon SageMaker by up to 18% for ml.p3 and ml.p2 GPU instances so you can maximize your ML budgets and accelerate innovation.
With new improvements to Amazon Personalize, we’ve reduced model training time by up to 40% and latency for generating recommendations by up to 30%. And with the improved efficiency of training Amazon Personalize, recommendations maintain relevance at a lower cost.
You can now use Amazon Rekognition for PPE detection to improve safety processes by automatically detecting if people in images are wearing PPE. Workplace safety is a top priority, especially in light of the COVID-19 pandemic. Even when people do their best to follow PPE guidelines, sometimes they inadvertently forget to wear PPE or don’t realize it’s required in the area they’re in.

Use cases

Get ideas and architectures from AWS customers, partners, ML Heroes, and AWS experts on how to apply ML to your use case:

Learn how to build a claims processing application for motor vehicle insurance that allows customers to submit an image of their vehicle with their insurance claim. You can train a simple computer vision model that detects if images are relevant to vehicle insurance claims using Amazon Rekognition Custom Labels and Amazon SageMaker Ground Truth.
Learn how zomato, a global food-tech company based in India, worked with the Amazon ML Solutions Lab to use Amazon Textract and Amazon SageMaker to recommend restaurants to zomato users based on searches for specific dishes.
Amazon Research Award winner Pittsburgh Health Data Alliance (PHDA) is using ML techniques to study breast cancer risk, identify depression markers, and understand what drives tumor growth, among other projects. See how over at Science.
Have you ever had a frustrating experience when contacting customer service? Autodesk wanted to solve this problem by using ML to direct customers to the right support team. Check out how they used Amazon SageMaker to build an ML skills model that reduced case misdirection by 30% in key support channels.
Many city cyclists are on the lookout for new ways to make cycling safer. Learn how to create a Smartcycle using two AWS DeepLens devices—one mounted on the front of your bicycle, the other mounted on the rear of the bicycle—to detect road hazards.

Explore more ML stories

Want more news about developments in ML? Check out the following stories:

How do you lead an organization through rapid change while keeping your customers at the forefront? Listen in to this Conversations with Leaders podcast where former Head of AI/ML for Zappos, Ameen Kazerouni, shares how he tackled that question, his thoughts on the core elements for approaching ML, investing in and up-skilling employees, and more.
The United States presidential election is days away. Dive into what candidates have said about the issues you care about using the Wall Street Journal’s recently launched Talk2020 tool. Talk2020 uses Amazon Kendra to allow you to search candidate transcripts using natural language capabilities. Start searching now and read more about how it was created.
This Wall Street Journal article examines the importance of ML in the advancement of healthcare. Discover how AWS customers like Livongo, Cambia Health, and Moderna are using ML to deliver higher-quality services and better patient outcomes.
With the increasing application of artificial intelligence and ML in sports analytics, AWS and Stats Perform partnered to bring ML-powered, real-time stats to the game of rugby, to enhance fan engagement and provide valuable insights into the game. Go the behind the scenes on the Kick Predictor, which predicts the probability of a successful penalty kick, computed in real time and broadcast live during the game.
With the help of Amazon ML Solutions Lab, the NFL’s Next Gen Stats team details how they developed a model to successfully predict the trajectories of defensive backs from when the pass is thrown to when the pass should arrive to the receiver.

Mark your calendars

Join us for the following exciting ML events:

If you missed it last month, be sure to catch up on SageMaker Fridays. Get started faster with machine learning with practical use cases and more, using Amazon SageMaker.
Registration is now open for re:Invent 2020. Don’t miss the machine learning keynote on December 8!

About the Author

Laura Jones is a product marketing lead for AWS AI/ML where she focuses on sharing the stories of AWS’s customers and educating organizations on the impact of machine learning. As a Florida native living and surviving in rainy Seattle, she enjoys coffee, attempting to ski and enjoying the great outdoors.