AWS Inferentia is a machine learning inference chip, custom designed by AWS to deliver high throughput, low latency inference performance at an extremely low cost. AWS Inferentia will support the TensorFlow, Apache MXNet, and PyTorch deep learning frameworks, as well as models that use the ONNX format.  

Amazon Managed Blockchain is a fully managed service that makes it easy to create and manage scalable blockchain networks using the popular open source frameworks Hyperledger Fabric and Ethereum. Hyperledger Fabric is available today, Ethereum is coming soon.

Amazon Elastic Inference allows you to attach just the right amount of GPU-powered acceleration to any Amazon EC2 and Amazon SageMaker instance to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon. 

Amazon DynamoDB on-demand is a flexible new capacity mode for DynamoDB capable of serving thousands of requests per second without capacity planning. DynamoDB on-demand offers simple pay-per-request pricing for read and write requests so that you only pay for what you use, making it easy to balance costs and performance.

Amazon QLDB is a fully managed ledger database that provides a transparent, immutable, and cryptographically verifiable transaction log ‎owned by a central trusted authority. Amazon QLDB tracks each and every application data change and maintains a complete and verifiable history of changes over time.

AWS Lake Formation is a service that makes it easy to set up a secure data lake in days. A data lake is a centralized, curated, and secured repository that stores all your data, both in its original form and prepared for analysis. A data lake enables you to break down data silos and combine different types of analytics to gain insights and guide better business decisions.

Amazon SageMaker Neo lets customers train models once, and run them anywhere with up to 2X improvement in performance. Applications running on connected devices at the edge are particularly sensitive to performance of machine learning models. They require low latency decisions, and are often deployed across a broad number of different hardware platforms. Amazon SageMaker Neo compiles models for specific hardware platforms, optimizing their performance automatically, allowing them to run at up to twice the performance, without any loss in accuracy. As a result, developers no longer need to spend time hand tuning their trained models for each and every hardware platform (saving time and expense). SageMaker Neo supports hardware platforms from NVIDIA, Intel, Xilinx, Cadence, and Arm, and popular frameworks such as Tensorflow, Apache MXNet, and PyTorch.

AWS Security Hub is a new service in Preview that gives you a comprehensive view of your high-priority security alerts and compliance status across AWS accounts.  With Security Hub, you now have a single place that aggregates, organizes, and prioritizes your security alerts, or findings, from multiple AWS services, such as Amazon GuardDuty, Amazon Inspector, and Amazon Macie, as well as from AWS Partner solutions.

Amazon Timestream is a purpose-built time series database service for collecting, storing, and processing time-series data such as server and network logs, sensor data, and industrial telemetry data for IoT and operational applications. Amazon Timestream processes trillions of events per day at one-tenth the cost of relational databases, with up to one thousand times faster query performance than a general-purpose database.

Amazon SageMaker now enables developers and data scientists to quickly and easily develop reinforcement learning models at scale with Amazon SageMaker RL.