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.

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