Amazon Relational Database Service (RDS) now supports AWS Graviton2-based database instances in preview. Graviton2 M6g and R6g database instances deliver better price performance over comparable current generation x86-based database instances. You can launch these database instances when using Amazon RDS for MySQL and Amazon RDS for PostgreSQL. Support for Amazon Aurora and Amazon RDS for MariaDB is coming soon.
Amazon Personalize enhances Recommendation Filters with filtering on item metadata
Amazon Personalize uses machine learning technology perfected from over 20 years of recommender systems development at Amazon.com. With Amazon Personalize you are can personalize recommendations for products, videos, music, ebooks, ads, marketing emails, and more, for your users, without any prior machine learning experience.
AWS CodeBuild supports code coverage reporting
AWS CodeBuild‘s support for Test Reporting now supports reporting for code coverage. Code coverage reports give you a detailed and actionable view of your test’s code coverage in CodeBuild, making it easier for you to identify the proportion and lines of code being tested.
Amazon S3 features now available in the AWS Toolkits for Visual Studio Code
Using the AWS Toolkit for VS Code, customers can now access Simple Storage Service (S3) resources in their account using the AWS Explorer view in the code editor. S3 integration makes it easy for customers to access S3 buckets and S3 objects in those buckets without leaving the VS Code interface. All CRUD (create, read, update, delete) operations for S3 can be performed: Creating objects in buckets, adding folders to buckets, deleting objects, and viewing the contents of objects.
AWS CodeBuild now supports parallel and coordinated executions of a build project
AWS CodeBuild now supports the execution of concurrent and coordinated builds of a project with “Batch” builds. Batch builds support the configuration and ordering of build executions with either a configuration list, configuration matrix, or a dependency-graph of build definitions. They’re intended for customers targeting different platforms or executing builds that depend on each other to produce artifacts.
Amazon EC2 On-Demand Capacity Reservations Now Support Windows BYOL
Amazon EC2 On-Demand Capacity Reservations now allows you to bring your own licenses (BYOL) of Windows operating systems. With this release, you can now create an EC2 Capacity Reservation and launch EC2 instances, running your existing Windows licenses, into the Capacity Reservation.
AWS Firewall Manager now supports centralized logging of AWS WAF logs
AWS Firewall Manager (FMS) now allows you to configure logging on your AWS WAF web ACLs centrally using an FMS policy. When you set up an FMS policy for AWS WAF, you can now enable logging on web ACLs for all the in-scope accounts and have the logs centralized under a single account.
Amazon Aurora Supports In-Place Upgrade from PostgreSQL 10 to 11
Starting today, you can upgrade your Amazon Aurora with PostgreSQL compatibility database cluster from major version 10 to 11, with just a few clicks in the AWS Management Console .
AWS Fargate for Amazon ECS now supports UDP load balancing with Network Load Balancer
You can now use a Network Load Balancer (NLB) to distribute UDP traffic to container-based applications running on AWS Fargate orchestrated by Amazon Elastic Container Service (ECS). Network Load Balancers are fully-managed load balancers that operate at the connection level (Layer-4) and are capable of handling millions of requests at ultra-low latency. Until now, you could use Network Load Balancers with AWS Fargate only with the TCP protocol. With this new integration, you get the simplicity of serverless containers to run applications on Fargate that use the UDP protocol. You can run workloads such as DNS, IoT, real-time media, and syslog while maintaining high throughput at ultra low latency through the Network Load Balancer.
AWS DeepComposer launches new learning capsule that deep dives into training an autoregressive CNN model
Today, we are excited to release a new learning capsule that deep dives into training an autoregressive convolutional neural network model (AR-CNN) in AWS DeepComposer. AWS DeepComposer gives developers a creative way to get started with machine learning (ML). With AWS DeepComposer, developers can get hands-on, literally, with a musical keyboard and the latest machine learning techniques to expand their ML skills. To learn the concepts of generative AI algorithms, developers can use easy-to-consume, bite-size learning capsules in the AWS DeepComposer console. In our previous learning capsule launched in June 2020, developers were introduced to an AR-CNN model and learned how it is able to add or remove one note at a time to generate music.