Remove Accountability Remove Events Remove Scripts
article thumbnail

Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning

Amazon Bedrock empowers teams to generate Terraform and CloudFormation scripts that are custom fitted to organizational needs while seamlessly integrating compliance and security best practices. Traditionally, cloud engineers learning IaC would manually sift through documentation and best practices to write compliant IaC scripts.

Scripts 123
article thumbnail

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

An AWS account and an AWS Identity and Access Management (IAM) principal with sufficient permissions to create and manage the resources needed for this application. If you don’t have an AWS account, refer to How do I create and activate a new Amazon Web Services account? The script deploys the AWS CDK project in your account.

APIs 119
article thumbnail

Implement backup and recovery using an event-driven serverless architecture with Amazon SageMaker Studio

AWS Machine Learning

Moreover, as of November 2022, Studio supports shared spaces to accelerate real-time collaboration and multiple Amazon SageMaker domains in a single AWS Region for each account. This post explains the backup and recovery module and one approach to automate the process using an event-driven architecture.

Scripts 66
article thumbnail

Implement Amazon SageMaker domain cross-Region disaster recovery using custom Amazon EFS instances

AWS Machine Learning

The data sync is completed by the Step Functions workflow, and its cadence can be on demand, scheduled, or invoked by an event. At the last step, a lifecycle config script is run to restore the Amazon EBS snapshots before the SageMaker space launched. Replace the account ID variable with your AWS account ID.

article thumbnail

Use your own training scripts and automatically select the best model using hyperparameter optimization in Amazon SageMaker

AWS Machine Learning

This post shows how Amazon SageMaker enables you to not only bring your own model algorithm using script mode, but also use the built-in HPO algorithm. We walk through the following steps: Use SageMaker script mode to bring our own model on top of an AWS-managed container. Solution overview. Find the metric in CloudWatch Logs.

Scripts 80
article thumbnail

Derive meaningful and actionable operational insights from AWS Using Amazon Q Business

AWS Machine Learning

QSI enables insights on your AWS Support datasets across your AWS accounts. First, as illustrated in the Linked Accounts group in Figure 1, this solution supports datasets from linked accounts and aggregates your data using the various APIs, AWS Lambda , and Amazon EventBridge. Synchronize the data source to index the data.

Scripts 113