9/19/2023, 12:00:00 AM ~ 9/20/2023, 12:00:00 AM (UTC)
Recent Announcements
Amazon QuickSight adds new layout and sparkline to KPI visual
Amazon QuickSight introduces a range of exciting enhancements to KPI visual, including templated KPI layouts, support for sparklines, improvements in conditional formatting, and a revamped format pane. The KPI visual now offers a user-friendly onboarding experience, allowing authors to select from pre-designed KPI layouts tailored to various use cases and configurations. This empowers authors to effortlessly craft visually appealing KPIs with just a few clicks.
Amazon RDS now supports X2iedn instances for SQL Server
Amazon RDS for SQL Server now supports memory optimized X2iedn DB instances that are well-suited for memory-intensive, read-heavy and high-throughput write operations.
Usability improvements and navigation bar enhancements for the AWS Management Console
Today, we launched four usability improvements and enhancements in the AWS Management Console. Usability improvements include descriptive page titles and high resolution favicons that display in the browser tab for an AWS Console page. We also updated the Navigation bar to include a display option for favorite icon size and a new settings menu. With this launch, the link to Unified Settings is moving from the account menu to the new settings menu shown as the gear icon in the navigation bar.
Announcing general availability of Amazon EC2 M2 Pro Mac instances for macOS
Starting today, Amazon Elastic Compute Cloud (Amazon EC2) M2 Pro Mac instances are now generally available (GA). These instances deliver up to 35% faster performance over the existing M1 Mac instances when building and testing applications for Apple platforms such as iOS, macOS, iPadOS, tvOS, watchOS, visionOS, and Safari. M2 Pro Mac instances are powered by the AWS Nitro System and are built on Apple M2 Pro Mac Mini computers featuring 12 core CPU, 19 core GPU, 32 GiB of memory, and 16 core Apple Neural Engine.
Announcing model retraining for Amazon Lookout for Equipment
Amazon Lookout for Equipment is an ML industrial equipment monitoring service that detects abnormal equipment behavior so you can act and avoid unplanned downtime. Lookout for Equipment uses your unique ML model(s) and in real-time helps accurately identify early warning signs that could lead to machine failures. This helps you detect equipment abnormalities with speed and precision, quickly diagnose issues, and take action to reduce expensive downtime.
AWS Blogs
AWS Japan Blog (Japanese)
- Monitor video content and encoding quality using AWS Elemental MediaConvert media metrics
- Weekly AWS — Week 2023/9/11
- Build an image search engine with Amazon Kendra and Amazon Rekognition
AWS News Blog
- New – Amazon EC2 M2 Pro Mac Instances Built on Apple Silicon M2 Pro Mac Mini Computers
- New – NVMe Reservations for Amazon Elastic Block Store io2 Volumes
AWS Cloud Operations & Migrations Blog
- Accelerating large-scale cloud migrations with repeatable patterns
- Automate insights for your EC2 fleets across AWS accounts and regions
AWS Big Data Blog
- Externalize Amazon MSK Connect configurations with Terraform
- How Chime Financial uses AWS to build a serverless stream analytics platform and defeat fraudsters
AWS Compute Blog
- Centralizing management of AWS Lambda layers across multiple AWS Accounts
- Implementing idempotent AWS Lambda functions with Powertools for AWS Lambda (TypeScript)
AWS Database Blog
- Run Amazon RDS for SQL Server 2x faster with X2iedn Instances
- Build aggregations for Amazon DynamoDB tables using Amazon DynamoDB Streams
- Amazon RDS for Oracle Transportable Tablespaces using RMAN
AWS HPC Blog
AWS for Industries
- Reinvent Your Telecom Business with AWS Telecommunications Competency Partners
- Cloud Technology Empowers Deutsche Telekom’s Fiber Optic Network – 60 percent of Core IT Applications of Deutsche Telekom IT Now Running on Cloud
AWS Machine Learning Blog
- Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets
- How VirtuSwap accelerates their pandas-based trading simulations with an Amazon SageMaker Studio custom container and AWS GPU instances
- Unlock ML insights using the Amazon SageMaker Feature Store Feature Processor