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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. This streamlines the ML workflows, enables better visibility and governance, and accelerates the adoption of ML models across the organization.

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

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon with a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Scripts 135
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Governing ML lifecycle at scale: Best practices to set up cost and usage visibility of ML workloads in multi-account environments

AWS Machine Learning

This post outlines steps you can take to implement a comprehensive tagging governance strategy across accounts, using AWS tools and services that provide visibility and control. Tagging is an effective scaling mechanism for implementing cloud management and governance strategies.

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How Deltek uses Amazon Bedrock for question and answering on government solicitation documents

AWS Machine Learning

This post provides an overview of a custom solution developed by the AWS Generative AI Innovation Center (GenAIIC) for Deltek , a globally recognized standard for project-based businesses in both government contracting and professional services. Deltek serves over 30,000 clients with industry-specific software and information solutions.

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Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

AWS Machine Learning

These customers need to balance governance, security, and compliance against the need for machine learning (ML) teams to quickly access their data science environments in a secure manner. We also introduce a logical construct of a shared services account that plays a key role in governance, administration, and orchestration.

APIs 93
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Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning

For now, we consider eight key dimensions of responsible AI: Fairness, explainability, privacy and security, safety, controllability, veracity and robustness, governance, and transparency. For early detection, implement custom testing scripts that run toxicity evaluations on new data and model outputs continuously.

APIs 122
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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

MLOps – Model monitoring and ongoing governance wasn’t tightly integrated and automated with the ML models. Reusability – Without reusable MLOps frameworks, each model must be developed and governed separately, which adds to the overall effort and delays model operationalization.