AWS Machine Learning Blog

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 […]

Read More

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.

Read More

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 […]

Read More

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 […]

Read More

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 […]

Read More

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

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 over 200 breakout sessions, hands-on workshops, deep-dive chalk talks, and more. You’ll hear success stories […]

Read More

Deploy your own TensorFlow object detection model to AWS DeepLens

In this blog post, we’ll show you how to deploy a TensorFlow object detection model to AWS DeepLens. This enables AWS DeepLens to perform real-time object detection using the built-in camera. Object detection is the technique for machines to correctly identify different objects in the image or video. Image recognition, specifically object detection is a […]

Read More

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 […]

Read More

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

Today, we’re unveiling a fresh approach to the AWS DeepLens Challenge. We are bringing you four challenges to choose from–sustainability, games, health and inclusivity. Now you can be inspired to create machine learning projects with AWS DeepLens and make a difference at the same time! Use these challenges to gain machine learning experience with fun, […]

Read More

Amazon SageMaker automatic model tuning produces better models, faster

Amazon SageMaker recently released a feature that allows you to automatically tune the hyperparameter values of your machine learning model to produce more accurate predictions. Hyperparameters are user-defined settings that dictate how an algorithm should behave during training. Examples include how large a decision tree should be grown, the number of clusters desired from a […]

Read More