AWS Machine Learning Blog

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 […]

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 as measured by the number of rings in the shell. The reticulate package will be used as an […]

Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Support for Amazon Translate API is now available in the AWS Mobile SDK for Android and iOS. Now, you can use the AWS Mobile SDK to develop and publish multilingual mobile apps quickly and easily with Amazon Translate. By […]

Today AWS released Model Server for Apache MXNet (MMS) v0.4, which adds support for serving Gluon models. Gluon is an imperative and dynamic interface for MXNet, which enables rapid model development, while maintaining MXNet performance. With this release, MMS adds support for packaging and serving Gluon models at scale. In this blog post, we will […]

Today, we are introducing Pipe input mode support for the Amazon SageMaker built-in algorithms. With Pipe input mode, your dataset is streamed directly to your training instances instead of being downloaded first. This means that your training jobs start sooner, finish quicker, and need less disk space. Amazon SageMaker algorithms have been engineered to be […]