11/30/2023, 12:00:00 AM ~ 12/1/2023, 12:00:00 AM (UTC)
Recent Announcements
myApplications: One place to view and manage your applications on AWS
Today, AWS announces the general availability of myApplications, a new experience in the AWS Management Console that makes it easier to manage and monitor the cost, health, security posture, and performance of your applications. Now, you can create your applications more easily and see your applications in an AWS account from one view in the AWS Management Console. With an at-a-glance view of key application metrics such as cost, performance, and security findings, you can debug operational issues and optimize your applications. You can also act on specific application resources with one click from the application dashboard using the corresponding AWS services, such as AWS Cost Explorer for cost, AWS Security Hub for security findings, and Amazon CloudWatch Application Signals for application performance.
Amazon SageMaker Studio is a single web-based interface with comprehensive machine learning (ML) tools and a choice of fully managed integrated development environments (IDEs) to perform every step of ML development, from preparing data to building, training, deploying, and managing ML models. Amazon EFS is a simple, serverless, set-and-forget, elastic file system that makes it easy to set up, scale, and cost-optimize file storage in the AWS Cloud. Today, we are excited to announce a new capability that allows you to bring you own EFS volume to access your large ML datasets or shared code from IDEs such as JupyterLab and Code Editor in SageMaker Studio.
Amazon SageMaker Distribution is now available on Code Editor based on Code-OSS and JupyterLab
Amazon SageMaker Studio offers fully integrated development environments (IDEs) for machine learning (ML). In July 2023, we launched Amazon SageMaker Distribution, a collection of docker images which includes the most popular libraries for ML on Amazon SageMaker Studio and Amazon Studio Lab. Today, we are extending the support for Amazon SageMaker Distribution on two popular IDEs used by data scientists and ML developers - Code Editor, based on Visual Studio Code Open Source (Code-OSS), and JupyterLab available on Amazon SageMaker Studio.
New and improved Amazon SageMaker Studio
Starting today, SageMaker Studio offers a suite of IDEs, including Code Editor based on Code-OSS Visual Studio Code Open Source, improved and faster JupyterLab, and RStudio. ML practitioners can choose their preferred IDE to accelerate ML development, for example, a data scientist could use JupyterLab and training jobs in Studio to explore data and tune models, while an MLOPs engineer could choose the Code Editor and the pipelines tool in Studio to deploy and monitor models in production. Your IDE will open in a separate tab allowing users to work with a full screen experience. Additionally, users can now view their training jobs, including jobs they may have scheduled from notebooks and training jobs they may have initiated from JumpStart. We are also excited to announce a new interactive experience in SageMaker Studio to deploy models with optimal configurations in as little as three clicks. Users can also now monitor and manage their endpoints in Studio without having to navigate to AWS Console. SageMaker Studio comes with an improved JumpStart experience. It is now easy to discover, import, fine tune and deploy a foundational model with just a few clicks.
Introducing an Integrated Development Environment (IDE) extension for AWS Application Composer
Today, Amazon Web Services, Inc. launches the general availability of Application Composer in VS Code, available as part of the AWS Toolkit. You can use AWS Application Composer’s drag-and-drop interface to create an application design from scratch or import an existing application definition to edit it.
Amazon SageMaker now provides a new setup and onboarding experience on AWS SageMaker console
Today, we are excited to announce a new onboarding and administration experience that makes it easy to setup and manage Amazon SageMaker domains. The setup and onboarding flow on console has been redesigned from the ground up to provide a friendlier one click experience for individual users and a step-by-step guide for Enterprise ML Administrators (Admins).
Amazon SageMaker Studio now provides a faster fully-managed notebooks in JupyterLab
Amazon SageMaker Studio is a single web-based interface with comprehensive machine learning (ML) tools and a choice of fully managed integrated development environments (IDEs) to perform every step of ML development, from preparing data to building, training, deploying, and managing ML models. Today, we are excited to announce a new and faster fully managed JupyterLab offering, the latest web-based IDE for notebooks, code, and data.
Announcing Code Editor, based on Code-OSS (VS Code – Open Source), in Amazon SageMaker Studio
Today, AWS announces the general availability of a new integrated development environment (IDE) option in Amazon SageMaker Studio: Code Editor, based on Code-OSS (Visual Studio Code – Open Source). You can now boost your analytics and machine learning (ML) teams’ productivity by using the lightweight and powerful IDE with its familiar shortcuts and terminal as well as its advanced debugging capabilities and refactoring tools.
AWS Fault Injection Service launches two highly requested scenarios
Today, AWS Fault Injection Service (FIS) announces the availability of two new scenarios, AZ Availability: Power Interruption and Cross-Region: Connectivity. The AZ Availability: Power Interruption scenario allows you to determine how a multi-AZ application will operate while experiencing the expected symptoms of a complete power interruption in a single AZ. The Cross-Region: Connectivity scenario helps you determine that a multi-Region application will operate as expected when the application cannot access resources in another Region.
Observe your applications with Amazon CloudWatch Application Signals (Preview)
Built on best practices from operating thousands of applications at Amazon, Amazon CloudWatch Application Signals (Preview) is a new capability that makes it easy to automatically instrument and operate applications on AWS. You can track application performance against your most important business objectives without the undifferentiated heavy lifting of manual instrumentation, metrics computations, and correlating observed problem to root cause. CloudWatch Application Signals provides standardized metrics such as volume, latency, and errors for each of your applications with pre built dashboards. In as few as three clicks, you can spot anomalies, drill into the most important metrics, and identify the root cause of issues with correlated metrics logs and traces.
Amazon Inspector enhances container image security by integrating with developer tools
Amazon Inspector now integrates with leading developer tools like Jenkins and TeamCity for container image assessments. This integration allows developers to assess their container images for software vulnerabilities within their Continuous Integration and Continuous Delivery (CI/CD) tools, pushing security earlier in the software development lifecycle. Assessment findings are conveniently available within the CI/CD tool’s dashboard, allowing developers to take automated actions in response to critical security issues, such as blocking builds or image pushes to container registries. You can use this feature by simply installing the Amazon Inspector plugin from your CI/CD tool marketplace and adding a step for Amazon Inspector scan in your build pipeline without needing to activate the Amazon Inspector service, provided you have an active AWS account. This feature works with CI/CD tools hosted anywhere, in AWS, on-premises, or hybrid clouds, providing consistency for developers to use a single solution across all their development pipelines.
Amazon Route 53 Application Recovery Controller launches zonal autoshift
Amazon Route 53 Application Recovery Controller now offers zonal autoshift, a feature that you can enable to safely and automatically shift your application’s traffic away from an AWS Availability Zone (AZ) when AWS identifies a potential failure affecting that AZ. For failures such as power and networking outages, zonal autoshift improves the availability of your application by shifting your application traffic away from an affected AZ to healthy AZs.
Announcing Solution Building Enablement for Partners
Today, AWS announces the availability of AWS Solution Building Enablement. This release provides AWS Partners prescriptive steps and self-serve assets to build and validate industry solutions across all business models.
AWS Partner CRM Connector now supports AWS Marketplace
AWS Partner CRM Connector now provides a modular experience allowing partners to enable both AWS Marketplace and AWS Partner Central ACE Pipeline Manager feature capabilities. The AWS Marketplace feature can be utilized to publish and manage AWS Marketplace private offers and resale authorizations. The ACE Pipeline Manager feature supports a newly enhanced opportunity management data model for AWS Partner co-sell motions with AWS. You can now utilize a dashboard feature of the AWS Partner CRM connector to view a summary of AWS Marketplace private offers. The AWS Partner CRM Connector enables you to centralize your sales operations in Salesforce so field sales representatives can create and publish AWS Marketplace private offers themselves with the permissions you give them, all within familiar tooling.
AWS Partner Network launches new Amazon EKS Ready Specialization
We are excited to announce the launch of the Amazon Elastic Kubernetes (EKS) Ready Specialization designed to highlight APN Partners who have demonstrated technical proficiency in providing solutions on running Kubernetes on Amazon Web Services (AWS), on-premises, and the edge with Amazon EKS and Amazon EKS-Anywhere (EKS-A).
Amazon Redshift now supports metadata security to simplify multi-tenant applications
Amazon Redshift now supports metadata security that enables administrators to restrict the visibility on their catalog data based on user roles and permissions. Users can now see only the metadata for databases, schema, and tables/views that they have access to. It enables customers to deploy multi-tenant applications on a provisioned cluster or Serverless namespace.
Announcing AWS Marketplace APIs for sellers
AWS Marketplace now provides new APIs for sellers, enabling independent software vendors (ISVs) and Channel Partners to build workflow integrations with AWS Marketplace for their AMI, SaaS, and Container products. With Catalog API, sellers can manage (create, update, and read) product listings, offers, and resale authorizations. With Agreements API, sellers can search and read agreements. Sellers can use the APIs to automate complex, high-volume, and business-critical transactions in AWS Marketplace, helping them save time and costs.
Amazon Redshift announces general availability of row-level security enhancements
Amazon Redshift now supports CONJUNCTION TYPE support for you row-level security (RLS) policies and RLS support on standard views and late binding views, which enables you to apply granular access controls and ensure that users only can access rows that they are authorized to see, even when the underlying data evolves or user permissions change.
Announcing SaaS Quick Launch for AWS Marketplace
Today, AWS announces the general availability of SaaS Quick Launch, a new deployment option for software-as-a-service (SaaS) products in AWS Marketplace. SaaS Quick Launch enables customers to reduce the time and resources required to configure and launch third-party SaaS products on AWS.
Announcing enhanced manageability and usability features for Amazon Redshift Serverless
Today, Amazon Redshift announces enhanced manageability and monitoring for features for Amazon Redshift Serverless, including cross-account cross-VPC, custom domain name(CNAME), snapshot scheduling, cross-region copy (CRC), improved visibility for serverless billing in the Redshift console, and version tracking. These features provide you with seamless data access, robust data protection, and cost-effective operations.
AWS Blogs
AWS Japan Blog (Japanese)
- Simple and comprehensive data protection with Amazon Data Lifecycle Manager
- Zero ETL integration with Amazon OpenSearch Service for Amazon DynamoDB is now available
AWS News Blog
- Use AWS Fault Injection Service to demonstrate multi-region and multi-AZ application resilience
- Zonal autoshift – Automatically shift your traffic away from Availability Zones when we detect potential issues
- IDE extension for AWS Application Composer enhances visual modern applications development with AI-generated IaC
- Amazon SageMaker Studio adds web-based interface, Code Editor, flexible workspaces, and streamlines user onboarding
- Three new capabilities for Amazon Inspector broaden the realm of vulnerability scanning for workloads
- Amazon CloudWatch Application Signals for automatic instrumentation of your applications (preview)
- New myApplications in the AWS Management Console simplifies managing your application resources
- Easily deploy SaaS products with new Quick Launch in AWS Marketplace
AWS Big Data Blog
- Integrate Okta with Amazon Redshift Query Editor V2 using AWS IAM Identity Center for seamless Single Sign-On
- Prepare and load Amazon S3 data into Teradata using AWS Glue through its native connector for Teradata Vantage
- How Eightfold AI implemented metadata security in a multi-tenant data analytics environment with Amazon Redshift
AWS Machine Learning Blog
- Welcome to a New Era of Building in the Cloud with Generative AI on AWS
- Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 2: Interactive User Experiences in SageMaker Studio
- Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 1: PySDK Improvements
- New – Code Editor, based on Code-OSS VS Code Open Source now available in Amazon SageMaker Studio
- Scale foundation model inference to hundreds of models with Amazon SageMaker – Part 1
- Reduce model deployment costs by 50% on average using the latest features of Amazon SageMaker
- Minimize real-time inference latency by using Amazon SageMaker routing strategies
- Build and evaluate machine learning models with advanced configurations using the SageMaker Canvas model leaderboard
- Introducing Amazon SageMaker HyperPod to train foundation models at scale
- Easily build semantic image search using Amazon Titan
- Evaluate large language models for quality and responsibility
- Accelerate data preparation for ML in Amazon SageMaker Canvas
- Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services
- Accelerate deep learning model training up to 35% with Amazon SageMaker smart sifting
AWS Security Blog
- Use CodeWhisperer to identify issues and use suggestions to improve code security in your IDE
- How to improve cross-account access for SaaS applications accessing customer accounts