AWS Machine Learning Blog

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 […]

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 […]

April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. We announced the AWS DeepLens Inclusivity Challenge two weeks […]

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 […]

A key barrier to the wider adoption of deep neural networks on industrial-size datasets is the time and resources required to train them. AlexNet, which won the 2012 ImageNet Large Scale Visual Recognition Competition (ILSVRC) and kicked off the current boom in deep neural networks, took nearly a week to train across the 1.2-million-image, 1000-category […]

Amazon SageMaker is a fully managed service that supports all of the steps of a ML model’s development: data exploration and building, training, and deploying ML models. With Amazon SageMaker, you can pick and use any of the built-in algorithms, reducing the time to market and the development cost.

December 2022: This post was reviewed and updated for accuracy. At AWS Machine Learning (ML) workshops, customers often ask, “After I deploy an endpoint, where do I go from there?” You can deploy an Amazon SageMaker trained and validated ML model as an online endpoint in production. Alternatively, you can choose which SageMaker functionality to […]