Today, we are announcing the general availability of myApplications supporting application operations, a new set of capabilities that help you get started with your applications on AWS, operate them with less effort, and move faster at scale. With myApplication in the AWS Management Console, you can more easily manage and monitor the cost, health, security posture, and performance of your applications on AWS. The myApplications experience is available in the Console Home, where you can access an Applications widget that lists the applications in an account. Now, you can create your applications more easily using the Create application wizard, connecting resources in your AWS account from one view in the console. The created application will automatically display in myApplications, and…
Tag: AWS News Blog
Easily deploy SaaS products with new Quick Launch in AWS Marketplace
Today we are excited to announce the general availability of SaaS Quick Launch, a new feature in AWS Marketplace that makes it easy and secure to deploy SaaS products. Before SaaS Quick Launch, configuring and launching third-party SaaS products could be time-consuming and costly, especially in certain categories like security and monitoring. Some products require hours of engineering time to manually set up permissions policies and cloud infrastructure. Manual multistep configuration processes also introduce risks when buyers rely on unvetted deployment templates and instructions from third-party resources. SaaS Quick Launch helps buyers make the deployment process easy, fast, and secure by offering step-by-step instructions and resource deployment using preconfigured AWS CloudFormation templates. The software vendor and AWS validate these templates…
Package and deploy models faster with new tools and guided workflows in Amazon SageMaker
I’m happy to share that Amazon SageMaker now comes with an improved model deployment experience to help you deploy traditional machine learning (ML) models and foundation models (FMs) faster. As a data scientist or ML practitioner, you can now use the new ModelBuilder class in the SageMaker Python SDK to package models, perform local inference to validate runtime errors, and deploy to SageMaker from your local IDE or SageMaker Studio notebooks. In SageMaker Studio, new interactive model deployment workflows give you step-by-step guidance on which instance type to choose to find the most optimal endpoint configuration. SageMaker Studio also provides additional interfaces to add models, test inference, and enable auto scaling policies on the deployed endpoints. New tools in SageMaker…
Use natural language to explore and prepare data with a new capability of Amazon SageMaker Canvas
Today, I’m happy to introduce the ability to use natural language instructions in Amazon SageMaker Canvas to explore, visualize, and transform data for machine learning (ML). SageMaker Canvas now supports using foundation model-(FM) powered natural language instructions to complement its comprehensive data preparation capabilities for data exploration, analysis, visualization, and transformation. Using natural language instructions, you can now explore and transform your data to build highly accurate ML models. This new capability is powered by Amazon Bedrock. Data is the foundation for effective machine learning, and transforming raw data to make it suitable for ML model building and generating predictions is key to better insights. Analyzing, transforming, and preparing data to build ML models is often the most time-consuming part…