This month in AWS Machine Learning: January edition
Hello and welcome to our first “This month in AWS Machine Learning” of 2021! 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.
We ended the year with more than 250 features launched in 2020, and January has kicked us off with even more new features for you to enjoy.
- AWS Contact Center Intelligence solutions are now available through multiple partners in EMEA, and contact center providers. Avaya, Talkdesk, Salesforce, and 8×8 now join Genesys as technology partners for AWS CCI.
- Reach new audiences, have more natural conversations, and develop and iterate faster, even in more than one language, with the new Amazon Lex V2 APIs. Check it out along with information on the new console.
Get ideas and architectures from AWS customers, partners, ML Heroes, and AWS experts on how to apply ML to your use case:
- Learn how Talkspace, a virtual therapy platform, integrated Amazon SageMaker and other AWS services to improve the quality of the mental healthcare it provides.
- Learn how AWS ML Hero Agustinus Nalwan helped make his toddler’s dream of flying come true with Amazon SageMaker.
- AWS ML can help you automate Paycheck Protection Program (PPP) loans and save small businesses across the US days of waiting for relief. Amazon Textract, Amazon Comprehend, Amazon Augmented AI (Amazon A2I), and Amazon SageMaker are helping our customers like BlueVine, Kabbage, Baker Tilly, and Biz2Credit process payment protection loans in hours versus days. Learn more about how you can automate loan processing.
- Amazon Fulfillment Technologies migrated from a legacy custom solution for identifying misplaced inventory to SageMaker, reducing AWS infrastructure costs by a projected 40% per month and simplifying its architecture.
- Learn how to build predictive disease models using SageMaker with data stored in Amazon HealthLake using two example predictive disease models.
- deepset explains how they’re building the next-level search engine for business documents using AWS an NVIDIA to achieve a speedup of 3.9 times faster and a cost reduction of 12.8 times less for training NLP models.
Explore more ML stories
Want more news about developments in ML? Check out the following stories:
- In our AWS Innovators series, we feature Chris Miller, who created a computer-controlled camera that uses a machine learning algorithm to detect and deter dogs who are pooping on your lawn.
- Commercial buildings are responsible for 40% of U.S. emissions. Learn how Carbon Lighthouse uses machine learning on AWS to develop insights that deliver energy savings and decrease CO2 emissions in commercial real estate.
- Explore how AWS customers like Koch, Woodside, and Bayer have leveraged machine learning in this WSJ article, The Next Industrial Revolution Is Powered by Machine Learning. And get more in-depth information on how Bayer helps farmers achieve more bountiful and sustainable harvests in this technical deep dive.
Mark your calendars
- If you missed AWS re:Invent 2020, you can watch sessions on demand and check out the first-ever ML keynote with Swami Sivasubramanian, VP of Machine Learning at AWS. And our AWS Heroes break down the keynote.
- The AWS DeepRacer pre-season launches today (February 1)! Register here and read more in this post.
- On Feb. 24, we are hosting the AWS Innovate Online Conference – AI & Machine Learning Edition, a free virtual event designed to inspire and empower you to accelerate your AI/ML journey. Whether you are new to AI/ML or an advanced user, AWS Innovate has the right sessions for you to apply AI/ML to your organization and take your skills to the next level. Register here.
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.