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

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.

Amazon Elastic Container Service (ECS) Capacity Providers are now available. Capacity Providers are a new way to manage compute capacity for containers, that allow the application to define its requirements for how it uses the capacity. With Capacity Providers, you can define flexible rules for how containerized workloads run on different types of compute capacity, and manage the scaling of the capacity. Capacity Providers improve the availability, scalability, and cost of running tasks and services on ECS.

We are announcing DJL, an open source library to develop Deep Learning models in Java. DJL offers user-friendly APIs to train, test, and deploy Deep Learning models. If you are a Java user interested in Deep Learning, DJL is a great way to start your journey. If you’re a Java developer working with Deep learning models, DJL will simplify the way you train and run predictions.

Amazon ECS, Amazon EKS, and AWS App Mesh now support AWS Outposts, a fully managed service that extends AWS infrastructure, and tools to virtually any datacenter, co-location space, or on-premises facility for a consistent hybrid experience.