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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 129
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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).

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Build a cross-account MLOps workflow using the Amazon SageMaker model registry

AWS Machine Learning

When designing production CI/CD pipelines, AWS recommends leveraging multiple accounts to isolate resources, contain security threats and simplify billing-and data science pipelines are no different. Some things to note in the preceding architecture: Accounts follow a principle of least privilege to follow security best practices.

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

AWS Machine Learning

Extensively used by data scientists and ML engineers across various industries, this robust tool provides high availability and uninterrupted access for its users. At the last step, a lifecycle config script is run to restore the Amazon EBS snapshots before the SageMaker space launched. Stop and restart the application.

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Build generative AI applications on Amazon Bedrock with the AWS SDK for Python (Boto3)

AWS Machine Learning

Solution overview The solution uses an AWS SDK for Python script with features that invoke Anthropic’s Claude 3 Sonnet on Amazon Bedrock. Prompt engineering techniques can improve FM performance and enhance results. Cost considerations depend on usage frequency, chosen model pricing, and resource utilization while the script runs.

Scripts 67
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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

Challenges in data management Traditionally, managing and governing data across multiple systems involved tedious manual processes, custom scripts, and disconnected tools. The diagram shows several accounts and personas as part of the overall infrastructure. The following diagram gives a high-level illustration of the use case.

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How Twilio used Amazon SageMaker MLOps pipelines with PrestoDB to enable frequent model retraining and optimized batch transform

AWS Machine Learning

PrestoDB is an open source SQL query engine that is designed for fast analytic queries against data of any size from multiple sources. Prerequisites To implement the solution provided in this post, you should have an AWS account , a SageMaker domain to access Amazon SageMaker Studio , and familiarity with SageMaker, Amazon S3, and PrestoDB.

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