AWS Machine Learning Blog

Financial institutions that extend credit face the dual tasks of evaluating the credit risk associated with each loan application and determining a threshold that defines the level of risk they are willing to take on. The evaluation of credit risk is a common application of machine learning (ML) classification models. The determination of a classification […]

With less than 40 days to re:Invent 2019, the excitement is building up and we are looking forward to seeing you all soon! Continuing our journey on artificial intelligence and machine learning, we are bringing a lot of technical content this year, with over 200 breakout sessions, deep-dive chalk talks, hands-on exercises with workshops featuring […]

Amazon Polly turns text into lifelike speech. In July 2019, AWS launched eight US English and three UK English voices in Neural Text-to-Speech (NTTS) technology, which delivers ground-breaking improvements in speech quality through a new machine learning approach. Polly is now adding the first non-English NTTS voices, in US Spanish and Brazilian Portuguese. Introducing Lupe […]

Today AWS is pleased to announce that it is working with Facebook, Microsoft, and the Partnership on AI on the first Deepfakes Detection Challenge.  The competition, to which we are contributing up to $1 million in AWS credits to researchers and academics over the next two years, is designed to produce technology that can be […]

The AWS DeepRacer League is the world’s first autonomous racing league, open to anyone. Announced at re:Invent 2018, it puts machine learning in the hands of every developer in a fun and exciting way. Throughout 2019, developers of all skill levels have competed in the League at 21 Amazon events globally, including Amazon re:MARS and select AWS Summits, and put their skills to the test in the League’s virtual circuit via the AWS DeepRacer console. The League concludes at re:Invent 2019. Log in today and start racing—time is running out to win an expenses paid trip to re:Invent!

This post looks at the role machine learning plays in providing fans with deeper insights into the game. We also provide code snippets that show the training and deployment process behind these insights on Amazon SageMaker.

In daily conversations, you often jump back and forth between multiple topics. For example, when discussing a home improvement project related to new windows and curtains, you might have questions like, “How about closing out on curtain styles and then revisiting colors?” When AWS launched Amazon Lex Session API, you learned how to address such […]

Building a highly accurate training dataset for your machine learning (ML) algorithm is an iterative process. It is common to review and continuously adjust your labels until you are satisfied that the labels accurately represent the ground truth, or what is directly observable in the real world. ML practitioners often built custom systems to review […]

Today, Amazon Web Services (AWS) announced that Amazon Textract, a machine learning service that quickly and easily extracts text and data from forms and tables in scanned documents, is now eligible for healthcare and life science workloads that require HIPAA compliance. This launch builds upon the existing portfolio of AWS artificial intelligence services that are […]

Ever been stumped by a question? Imagine you’re in a business review going over weekly numbers and someone asks, “What about expenses?” Your response might be, “I don’t know. I wasn’t prepared to have that discussion right now.” Bots aren’t fortunate enough to have the same comprehension capabilities, so how should they respond when they […]