AWS Machine Learning Blog

Thorn is a non-profit organization dedicated to stopping the spread of child sexual abuse material and standing up to child traffickers. In 2017, Thorn’s tools were 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 […]

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 […]

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 blog post we’ll describe how you can search for a document using voice or text.  […]

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 […]

This blog shares some brief thoughts on machine learning accuracy and bias. Let’s start with some comments about a recent ACLU blog in which they ran a facial recognition trial. Using Rekognition, the ACLU built a face database using 25,000 publicly available arrest photos and then performed facial similarity searches on that database using public […]

The AWS Deep Learning AMIs (DLAMI) for Ubuntu and Amazon Linux are now pre-installed and fully configured with Open Neural Network Exchange (ONNX), enabling model portability across deep learning frameworks. In this blog post we’ll introduce ONNX, and demonstrate how ONNX can be used on the DLAMI to port models across frameworks. What is ONNX? ONNX is an open […]

We announced the AWS DeepLens Inclusivity Challenge two weeks ago, and how time has flown! In this first challenge, we invite you to create an AWS DeepLens project that fosters inclusion for people of all abilities and helps individuals overcome barriers associated with special needs. For each project that meets the participation criteria, we will […]

The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come with an optimized build of TensorFlow 1.9 custom-built directly from source and fine-tuned for high performance training across Amazon EC2 instances. In addition, the AMIs come with the latest Apache MXNet 1.2 with several performance and usability improvements, the new Keras 2-MXNet backend […]