AWS Security Hub is now integrated with AWS Audit Manager, which helps simplify how you assess risk and monitor your compliance with regulations and industry standards. AWS Audit Manager is a new service that helps you continuously audit your AWS usage and automates evidence collection to make it easier for you to assess whether your policies, procedures, and activities are operating effectively. Using a prebuilt or customized framework, you can launch an Audit Manger assessment to begin collecting and organizing evidence, such as Security Hub findings, in accordance with the requirements of an industry standard or regulation, such as the Payment Card Industry Data Security Standard (PCI DSS) or the Center for Internet Security (CIS) AWS Foundations Benchmark standard. With Audit Manager, you can focus on reviewing the relevant evidence to ensure your controls are working as intended and build audit-ready reports with much less manual effort. For more information on AWS Audit Manager, see their documentation here .

We’re excited to announce Amazon SageMaker Feature Store, a new capability of Amazon SageMaker to ingest, store, share, reuse, and serve features for real time and batch machine learning (ML) applications.  

We’re excited to announce Amazon SageMaker Pipelines, a new capability of Amazon SageMaker to build, manage, automate, and scale end to end machine learning workflows. SageMaker Pipelines brings automation and orchestration to ML workflows, enabling you to accelerate machine learning projects and scale up to thousands of models in production.

Today we are introducing Amazon SageMaker Clarify to help machine learning developers achieve greater visibility into their training data and models so they can identify and limit bias and explain predictions.

Amazon HealthLake is a HIPAA-eligible service that enables healthcare providers, health insurance companies, and pharmaceutical companies to store, transform, query, and analyze health data at petabyte scale.

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. With Amazon SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from a single visual interface. 

Amazon Braket provides a fully managed and high-performance tensor network simulator (TN1). This tensor-network-based circuit simulator can support quantum computing simulations with up to 50 qubits, and is particularly powerful for sparse circuits, circuits with local gates, and other circuits with inherent structure.

Amazon Elastic Container Registry (Amazon ECR) now supports cross region replication of images in private repositories, enabling developers to easily copy container images across multiple AWS accounts and regions with a single push to a source repository. Storing images in-region to your infrastructure helps applications start up faster as image download time is reduced due to lower latency, and removes cross region downloads that helps with region isolation. Geographically dispersed images also helps you meet backup and disaster recovery requirements for your application. By creating a simple way to reliably replicate images across regions, Amazon ECR now makes it even easier to run highly available applications in AWS. 

Amazon CodeGuru Profiler now profiles your application’s memory, giving you a consolidated view of the heap. The heap summary shows all objects allocated on the heap during a given time frame. For each object (e.g. String, int, char[], custom types, etc.) you can see a summed-up size and number of objects. These metrics are also presented in a time series so you can see how the object size or count changes over time.