AWS Machine Learning Blog

Accelerate model training using faster Pipe mode on Amazon SageMaker

Amazon SageMaker now comes with a faster Pipe mode implementation, significantly accelerating the speeds at which data can be streamed from Amazon Simple Storage Service (S3) into Amazon SageMaker while training machine learning models. Pipe mode offers significantly better read throughput than the File mode that downloads data to the local Amazon Elastic Block Store […]

Amazon SageMaker Neural Topic Model now supports auxiliary vocabulary channel, new topic evaluation metrics, and training subsampling

In this blog post, we introduce three new features of the Amazon SageMaker Neural Topic Model (NTM) that are designed to help improve user productivity, enhance topic evaluation capability, and speed up model training. In addition to these new features, by optimizing sparse operations and the parameter server, we have improved the speed of the […]

Beyond text: How Spokata uses Amazon Polly to make news and information universally accessible as real-time audio

Just as television transitioned from black and white to color, the web has been moving from a text-based medium to one dominated by sound and vision. Accordingly, content creation has both exploded and changed. Publishers of all types are struggling through this transition as they try to meet the demands of users while keeping their business models intact.

On-demand audio is attracting significant interest from publishers as mobile listening grows and in-car technology begins to disrupt traditional radio. This trend is most visible in the mainstream adoption of podcasts. But podcasts are just the beginning of a rapidly emerging, and diverse, ecosystem of new digital audio formats. Amazon Echo and advanced text-to-speech services such as Amazon Polly are enabling the creation of these new audio products.

In this blog post we describe how Spokata leverages these Amazon technologies to make text-based news and information universally accessible as real-time audio.

Segmenting brain tissue using Apache MXNet with Amazon SageMaker and AWS Greengrass ML Inference – Part 2

In Part 1 of this blog post, we demonstrated how to train and deploy neural networks to automatically segment brain tissue from an MRI scan in a simple, streamlined way using Amazon SageMaker. We used Apache MXNet to train a convolutional neural network (CNN) on Amazon SageMaker using the Bring Your Own Script paradigm. We […]

How to use common workflows on Amazon SageMaker notebook instances

Amazon SageMaker notebook instances provide a scalable cloud based development environment to do data science and machine learning. This blog post will show common workflows to make you more productive and effective. The techniques in this blog post will give you tools to treat your notebook instances in a more cloud native way, remembering that […]

PyTorch 1.0 preview now available in Amazon SageMaker and the AWS Deep Learning AMIs

Amazon SageMaker and the AWS Deep Learning AMIs (DLAMI) now provide an easy way to evaluate the PyTorch 1.0 preview release. PyTorch 1.0 adds seamless research-to-production capabilities, while retaining the ease-of-use that has enabled PyTorch to rapidly gain popularity. The AWS Deep Learning AMI comes pre-built with PyTorch 1.0, Anaconda, and Python packages, with CUDA and […]

Your Guide to AI and Machine Learning at re:Invent 2018

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. re:Invent 2018 is almost here! As you plan your agenda, artificial intelligence (AI) is undoubtedly a hot topic on your list. This year we have a lot of great technical content on AI, machine learning (ML), and deep learning (DL)—with […]

Deploy your own TensorFlow object detection model to AWS DeepLens

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. In this blog post, we’ll show you how to […]

Segmenting brain tissue using Apache MXNet with Amazon SageMaker and AWS Greengrass ML Inference – Part 1

Annotation and segmentation of medical images is a laborious endeavor that can be automated in part via deep learning (DL) techniques. These approaches have achieved state-of-the-art results in generic segmentation tasks, the goal of which is to classify images at the pixel level. In Part 1 of this blog post, we demonstrate how to train […]

Help improve lives through Machine Learning by joining the AWS DeepLens Challenge!

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. Today, we’re unveiling a fresh approach to the AWS […]