AWS Machine Learning Blog

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 […]

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 […]

The Keras-MXNet deep learning backend is available now, thanks to contributors to the Keras and Apache MXNet (incubating) open source projects. Keras is a high-level neural network API written in Python. It’s popular for its fast and easy prototyping of CNNs and RNNs. Keras developers can now use the high-performance MXNet deep learning engine for […]

In this blog post, we conduct a performance comparison for PCA using Amazon SageMaker, Spark ML, and Scikit-Learn on high-dimensional datasets. SageMaker consistently showed faster computational performance. Refer Figures (1) and (2) at the bottom to see the speed improvements. Principal Component Analysis Principal Component Analysis (PCA) is an unsupervised learning algorithm that attempts to […]

Amazon Polly is a service that turns text into lifelike speech. Using Amazon Polly you can create applications that talk and build entirely new categories of speech-enabled products. Starting today, you can apply to participate in a developer preview that allows you to use eight English (U.S.) Amazon Polly voices to narrate your Alexa skills.

If your skill uses only a single voice today, you can try changing the voice or adding different voices in the right places to provide an even more engaging experience. Developers in the preview can select a different voice for any utterance by constructing output speech using the Structured Speech Markup Language (SSML) and specifying an Amazon Polly voice using the voice tag for free in Alexa skills. Note that SSML tags that are only available through the Amazon Polly service and not through Alexa skills will not be available when you use this new capability.