AWS Machine Learning Blog
Maximize training performance with Gluon data loader workers
With recent advances in CPU and GPU technology, training complex and deep neural network models in a few hours is within reach for many state of-the-art deep models. However, when you use a system with such high processing throughput potential, the required data for the processing pipeline must be ready before each iteration.
Use the Amazon SageMaker local mode to train on your notebook instance
This blog post shows you how to use the Amazon SageMaker Python SDK local mode on a recently launched multi-GPU notebook instance type to quickly test a large scale image classification model.
AWS Deep Learning AMIs now with optimized Chainer 4 and CNTK 2.5.1 to accelerate deep learning on Amazon EC2 instances
The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come with Chainer 4 and Microsoft Cognitive Toolkit (CNTK) 2.5.1 configured with optimizations for higher performance execution across Amazon EC2 instances.
Use the built-in Amazon SageMaker Random Cut Forest algorithm for anomaly detection
Today, we are launching support for Random Cut Forest (RCF) as the latest built-in algorithm for Amazon SageMaker. RCF is an unsupervised learning algorithm for detecting anomalous data points or outliers within a dataset. This blog post introduces the anomaly detection problem, describes the Amazon SageMaker RCF algorithm, and demonstrates the use of the Amazon […]
Amazon Polly achieves HIPAA eligibility
Amazon Polly is a service that turns text into lifelike speech, allowing you to create applications that talk, and build entirely new categories of speech-enabled products. The API-based Text-to-Speech service is now HIPAA eligible.
Easily perform facial analysis on live feeds by creating a serverless video analytics environment using Amazon Rekognition Video and Amazon Kinesis Video Streams
In this blog post, we’ll use your webcam on your laptop to send a live feed to an Amazon Kinesis Video Stream. From there, a processor within Amazon Rekognition Video analyzes the feed and compares it to a collection we create. The output matches will get sent to us via an email through an integration with AWS Lambda and Amazon Simple Notification Service (Amazon SNS).
Build text analytics solutions with Amazon Comprehend and Amazon Relational Database Service
In this blog post, we will show you how to get started building rich text analytics views from your database, without having to learn anything about machine learning for natural language processing models. We’ll do this by leveraging Amazon Comprehend, paired with Amazon Aurora-MySQL and AWS Lambda.
Build automatic analysis of body language to gauge attention and engagement using Amazon Kinesis Video Streams and Amazon AI Services
This is a guest blog post by Ned T. Sahin, PhD (Brain Power LLC and Harvard University), Runpeng Liu (Brain Power LLC and MIT), Joseph Salisbury, PhD (Brain Power LLC), and Lillian Bu (Brain Power LLC and MIT). Producers of content (from ads to games to teaching materials) usually judge the success of their content […]
Use facial recognition to deliver high-end consumer experience with Amazon Kinesis Video Streams and Amazon Rekognition Video
Whatever your use case, real-time face recognition with Kinesis Video Streams and Rekognition Video is easy to set up and doesn’t require expensive hardware. The entire system built here is serverless and Rekognition Video qualifies for the AWS Free Tier.
AWS Deep Learning AMIs now with optimized TensorFlow 1.7 for faster training on Amazon EC2 C5 and P3 instances
The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come with TensorFlow 1.7, which is built with advanced optimizations for high-performance training across Amazon EC2 instance families. This is an update to the optimized build of TensorFlow 1.6 that we launched in late March. Faster training with optimized TensorFlow 1.7 The Amazon Machine […]