The AWS Deep Learning AMIs are available on Ubuntu 18.04 , Ubuntu 16.04 , Amazon Linux 2 , and Amazon Linux with TensorFlow 1.15, Tensorflow 2.0, PyTorch 1.3.1, MXNet 1.6.0-rc0. Also new in this version is support for AWS Neuron, a SDK for running inference using AWS Inferentia chips. It consists of a compiler, run-time, and profiling tools that enable developers to run high-performance and low latency inference using Inferentia-based EC2 Inf1 instances. Neuron is pre-integrated into popular machine learning frameworks including TensorFlow, Pytorch, and MXNet to deliver optimal performance of EC2 Inf1 instances. Customers using Amazon EC2 Inf1 instances will receive the highest performance and lowest cost for machine learning inference in the cloud, and no longer need to make the sub-optimal tradeoff between optimizing for latency or throughput when running large machine learning models in production.  

Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities such as online payment fraud and the creation of fake accounts. Fraud Detector uses machine learning (ML) and 20 years of fraud detection expertise from AWS and Amazon.com to automatically identify potentially fraudulent activity so you can catch more fraud faster. With Fraud Detector, you can create a fraud detection model with just a few clicks and no prior ML experience because Fraud Detector handles all of the ML heavy lifting for you.

Today, we are announcing the general availability of Amazon EC2 Inf1 instances, built from the ground up to support machine learning inference applications. Inf1 instances feature up to 16 AWS Inferentia chips, high-performance machine learning inference chips designed and built by AWS. In addition, we’ve coupled the Inferentia chips with the latest custom 2nd Gen Intel® Xeon® Scalable processors and up to 100 Gbps networking to enable high throughput inference. This powerful configuration enables Inf1 instances to deliver up to 3x higher throughput and up to 40% lower cost per inference than Amazon EC2 G4 instances, which were already the lowest cost instance for machine learning inference available in the cloud.

Amazon SageMaker Experiments is a new capability that lets you organize, track, and compare your machine learning training experiments on Amazon SageMaker.

Amazon Elasticsearch Service now offers UltraWarm, a performance-optimized warm storage tier. UltraWarm lets you store and interactively analyze your data using Elasticsearch and Kibana while reducing your cost per GB by up to 90% over existing Amazon Elasticsearch Service hot storage options. With UltraWarm, Amazon Elasticsearch Service now supports hot-warm domain configurations. Hot storage is used for indexing and providing the fastest access to data. UltraWarm complements hot storage with less expensive, more durable storage for older data that you access less frequently, all while maintaining the same interactive analysis experience.

Amazon EMR is now available on AWS Outposts, allowing you to deploy open-source tools like Apache Spark and Apache Hive in your data center. Using Amazon EMR on AWS Outposts, you can set up, deploy, manage, and scale Apache Hadoop, Apache Hive, Apache Spark, and Presto clusters in your on-premises environments, just as you would in the cloud. Amazon EMR on AWS Outposts provides cost-efficient capacity while automating time-consuming administration tasks like infrastructure provisioning, cluster setup, configuration, or tuning, freeing you to focus on your applications.

Amazon SageMaker has launched the public preview of a new notebook experience that allows developers to spin up machine learning notebooks in seconds, and enables sharing of notebooks with just a single click. The new experience is available via SageMaker Studio, a fully integrated development environment for machine learning.

Amazon SageMaker Model Monitor is a new capability of Amazon SageMaker that continuously monitors machine learning (ML) models in production, detects deviations such as data drift that can degrade model performance over time, and alerts you to take remedial actions.

Amazon SageMaker Debugger is a new capability of Amazon SageMaker that provides complete insights into the training process of machine learning (ML) models by automating the capture and analysis of data from training runs at real time, with no code changes.

The AWS Deep Learning Containers are available today with the latest framework versions of Tensorflow 1.15, PyTorch 1.3.1, and MXNet 1.6.0-rc0. You can launch the new versions of Deep Learning Container on Amazon Sagemaker, Amazon Elastic Kubernetes Service (Amazon EKS), self-managed Kubernetes on Amazon EC2, and Amazon Elastic Container Service (Amazon ECS). For a complete list of frameworks and versions supported by the AWS Deep Learning Containers, see release notes.