Amazon SageMaker Processing is a new capability of Amazon SageMaker for running pre- or post- processing and model evaluation workloads with a fully managed experience.

Amazon Relational Database Service (Amazon RDS) on Outposts is available in Preview, allowing you to deploy fully managed Amazon RDS database instances in your on-premises environments. AWS Outposts bring native AWS services, infrastructure, and operating models to virtually any data center, co-location space, or on-premises facility. You can deploy Amazon RDS on Outposts to set up, operate, and scale relational databases on premises, just as you would in the cloud. Amazon RDS provides cost-efficient and resizable capacity for databases in Outposts, while automating time-consuming administration tasks including infrastructure provisioning, database setup, patching, and backups, freeing you to focus on your applications.

We are excited to announce the availability of Accelerated Site-to-Site VPN which uses AWS Global Accelerator to improve the performance of VPN connections by intelligently routing traffic through the AWS Global Network and AWS edge locations.

You can now use Amazon Elastic Kubernetes Service (EKS) to run Kubernetes pods on AWS Fargate, the serverless compute engine built for containers on AWS. This makes it easier than ever to build and run your Kubernetes applications in the AWS cloud. 

Amazon SageMaker support for the Deep Graph Library (DGL) is now available. With DGL, you can improve the prediction accuracy of recommendation, fraud detection, and drug discovery systems using Graph Neural Networks (GNNs).

Today we are announcing AWS Compute Optimizer, a new machine learning-based recommendation service that makes it easy for you to ensure that you are using optimal AWS Compute resources.  

Starting today, Amazon Redshift RA3 nodes with managed storage are generally available. RA3 nodes enable you to scale and pay for compute and storage independently allowing you to size your cluster based only on your compute needs. Now you can analyze even more data cost-effectively.

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.