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.

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. 

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.

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 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.

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 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.

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.

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. 

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.