AWS Machine Learning Blog

We are excited to announce the general availability of the Amazon Lex and Genesys PureCloud integrated interactive voice response (IVR) solution. First launched in preview at re:Invent 2017, the solution allows Genesys customers to integrate Amazon Lex chatbots into the PureCloud  IVR flows to provide an enhanced conversational experience. About Genesys PureCloud The Genesys PureCloud platform […]

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. Amazon Rekognition is now available in the Asia Pacific (Seoul) and Asia Pacific (Mumbai) AWS Regions. Amazon Rekognition is a deep learning-based image and video analysis service that can identify objects, people, text, scenes, and activities, as well as detect […]

The Amazon SageMaker pre-built MXNet container now uses the latest release of Apache MXNet 1.2.  Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.  And the pre-built MXNet container makes it easy to write your deep learning scripts naturally […]

By providing a scalable cloud platform for building communications experiences, Twilio enables developers and businesses to build any customer engagement into their applications using simple and powerful APIs for voice, messaging, and video. Businesses like Morgan Stanley, Marks & Spencer, Netflix, Lyft, Airbnb, and more than 50,000 others are modernizing the way they communicate with […]

Thorn is a non-profit organization dedicated to stopping the spread of child sexual abuse material and standing up to child traffickers. Thorn’s tools have been used to identify 5,894 child sex trafficking victims and rescue 103 children where their sexual abuse was recorded and distributed. Using AWS services such as Amazon Rekognition, Thorn has seen […]

Not only does Amazon SageMaker  provide easy scalability and distribution to train and host ML models, it is modularized so that the process of training a model is decoupled from deploying the model. This means that models that are trained outside of Amazon SageMaker can be brought into SageMaker only to be deployed. This is very useful […]

Supervised learning needs labels, or annotations, that tell the algorithm what the right answers are in the training phases of your project. In fact, many of the examples of using MXNet, TensorFlow, and PyTorch start with annotated data sets you can use to explore the various features of those frameworks. Unfortunately, when you move from […]

Amazon Polly is an AWS service that turns text into lifelike speech. We’re excited to announce new Hindi language support and the release of our first bilingual voice. Aditi is a female voice that speaks Hindi and Indian English fluently. Let’s hear Aditi introduce herself in both Indian English and Hindi. Listen to the Hindi […]

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. People spend a lot of time searching documents. First you go to your document store and then you search for relevant documents. If you’re looking for a text inside the document, then you need to do another search. In this […]

Data scientists and developers can use the Amazon SageMaker fully managed machine learning service to build and train machine learning (ML) models, and then directly deploy them into a production-ready hosted environment. In this blog post we’ll show you  how to use Amazon SageMaker to do transfer learning using a TensorFlow container with our own […]