This month in AWS Machine Learning: September 2020 edition

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This month in AWS Machine Learning: September 2020 edition

Every day there is something new going on in the world of AWS Machine Learning—from launches to new 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 native support for TorchServe in Amazon SageMaker, launched a new NFL Next Gen Stat, and enhanced our language services including Amazon Transcribe and Amazon Comprehend. Read on for our September launches:

 TorchServe is now natively supported in Amazon SageMaker as the default model server for PyTorch inference to help you bring models to production quickly without having to write custom code.

• With the new automatic language detection feature for Amazon Transcribe, you can now simply provide audio files and Amazon Transcribe detects the dominant language from the speech signal and generates transcriptions in the identified language. Amazon Transcribe now also expands support for Channel Identification to streaming audio transcription. With Channel Identification, you can process live audio from multiple channels, and produce a single transcript of the conversation with channel labels.
• You can now use Amazon Comprehend to detect and redact personally identifiable information (PII) in customer emails, support tickets, product reviews, social media, and more.
• To kick off football season, AWS and the National Football League announced new Next Gen Stats powered by AWS. One of those stats, Expected Rushing Yards, was developed at the NFL’s Big Data Bowl, powered by AWS. Expected Rushing Yards is designed to show how many rushing yards a ball carrier is expected to gain on a given carry based on the relative location, speed, and direction of blockers and defenders.

Use cases

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

• See how the University of Illinois is employing its work with the Amazon ML Solutions Lab to use a data-driven approach and ML to coach at the collegiate level.
• Learn how to prevent customer churn by optimizing incentive programs using stochastic programming.
• Find out how Kabbage developed an automated loan review process using Amazon Textract to provide over 500,000 small businesses access to more than $16 billion of working capital, including the Paycheck Protection Program (PPP).
• Get the scoop on how companies like Renga, Inc and DevFactory are getting highly precise, automated code reviews with Amazon CodeGuru.
• Learn how to create an automated and serverless content-processing pipeline for analyzing text in PDF documents using Amazon Textract and translating them with Amazon Translate.

Explore more ML stories

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

• DBS Bank is training 3,000 employees in AI with the help of the AWS DeepRacer League. Watch to see how they are developing ML skills and fostering community for their workforce.
• Amazon scientist Amaia Salvador shared her views on the challenges facing women in computer vision in a recent Science article.
• Aella Credit is making banking more accessible using AWS ML.

• Read about how ML is identifying and tracking pandemics like COVID-19, and check out this VentureBeat Webinar featuring Michelle Lee, VP of the Amazon ML Solutions Lab, on how companies are finding new ways to operate and respond in this pandemic with AI/ML.
• Build, Train and Deploy a real-world flower classifier of 102 flower types with instruction from this tutorial by AWS ML Community Builder Juv Chan.
• Earlier this month, we announced that Lena Taupie, a software developer for Blubrr, won the AWS DeepComposer Chartbusters challenge. Learn about Lena’s experience competing in the challenge and how she drew from her own background, having grown up in St. Lucia, a city with a history of oral and folk traditional music, to develop a new custom genre model using Generative AI.

Mark your calendars

Join us for the following exciting ML events:

• SageMaker Fridays is back with season 2 and it is going to be bigger. You can get started faster with machine learning using Amazon SageMaker, where we will discuss practical use cases and applications through the season. Join us on the season premiere on Oct. 9, and we will continue to meet every week with a new use case.
• On October 18, join Quantiphi for a conversation on how you can add AI to your contact centers to drive better customer engagement. Register now!
• AWS is sponsoring the WSJ Pro AI Forum on October 28. Register to learn strategies for bringing AI and ML to your organization.

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.