AWS Machine Learning Blog

Today we are launching the training job cloning feature on the Amazon SageMaker console, which makes it much easier for you to create training jobs based on existing ones. When you use Amazon SageMaker, it’s common to run multiple training jobs using different training sets and identical configuration. It’s also common to adjust a specific […]

The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come pre-installed and fully configured with Horovod, the popular open source distributed training framework to scale TensorFlow training on multiple GPUs. This is an update to the optimized build of TensorFlow 1.8 that we launched in early May. This custom build of TensorFlow 1.8 […]

Amazon Polly now offers a choice of a second female French voice, Léa, in addition to the current female voice, Céline. Amazon Polly also has a French male voice, Mathieu. Léa is a warm and natural-sounding voice with Parisian accent. Listen to the spoken introduction from Léa. Listen now Voiced by Amazon Polly With the addition […]

Today consumers are encouraged to express their satisfaction or frustration with a company or product through social media, blogs, and review platforms. Sentiment analysis can help companies better understand their customers’ opinions and needs and make more informed business decisions. Amazon released a dataset to the public with over 130 million product reviews in multiple […]

At the beginning of this year, we launched an Amazon Polly plugin for WordPress with the help of our partner, WP Engine. This plugin enabled creators/bloggers who publish their posts or sites using WordPress to easily add Text-to-Speech capabilities and convert their web content into high-quality audio formats. Since then, we’ve improved the plugin by […]

We have seen a lot of discussion this past week about the role of Amazon Rekognition in facial recognition, surveillance, and civil liberties, and we wanted to share some thoughts.

Amazon Rekognition is a service we announced in 2016. It makes use of new technologies – such as deep learning – and puts them in the hands of developers in an easy-to-use, low-cost way. Since then, we have seen customers use the image and video analysis capabilities of Amazon Rekognition in ways that materially benefit both society (e.g. preventing human trafficking, inhibiting child exploitation, reuniting missing children with their families, and building educational apps for children), and organizations (enhancing security through multi-factor authentication, finding images more easily, or preventing package theft). Amazon Web Services (AWS) is not the only provider of services like these, and we remain excited about how image and video analysis can be a driver for good in the world, including in the public sector and law enforcement.

Parallel processing with multiple GPUs is an important step in scaling training of deep models. In each training iteration, typically a small subset of the dataset, called a mini-batch, is processed. When a single GPU is available, processing of the mini-batch in each training iteration is handled by this GPU. When training with multiple GPUs, […]

The most common challenge we can face when traveling abroad is the language barrier. Whether lost or not, we’ll have to say at least one of these: “Where is the best place to eat and drink?”, “Where is this hotel?”, and “Where is the bathroom?” Now imagine a more difficult scenario: We’re traveling to Spain […]

Once you have trained, optimized and deployed your machine learning (ML) model, the next challenge is to host it in such a way that consumers can easily invoke and get predictions from it. Many customers have consumers who are either external or internal to their organizations and want to use the model for predictions (ML […]

July, 2022: This post was reviewed and updated for relevancy and accuracy, with an updated AWS CloudFormation Template. December 2020: Post updated with changes required for Amazon SageMaker SDK v2 This blog post describes how to train, deploy, and retrieve predictions from a machine learning (ML) model using Amazon SageMaker and R. The model predicts abalone age […]