article thumbnail

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

AWS Machine Learning

For a multi-account environment, you can track costs at an AWS account level to associate expenses. A combination of an AWS account and tags provides the best results. Tagging is an effective scaling mechanism for implementing cloud management and governance strategies.

article thumbnail

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Human oversight : Including human involvement in AI decision-making processes.

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

Improve governance of models with Amazon SageMaker unified Model Cards and Model Registry

AWS Machine Learning

You can now register machine learning (ML) models in Amazon SageMaker Model Registry with Amazon SageMaker Model Cards , making it straightforward to manage governance information for specific model versions directly in SageMaker Model Registry in just a few clicks.

article thumbnail

Governing the ML lifecycle at scale: Centralized observability with Amazon SageMaker and Amazon CloudWatch

AWS Machine Learning

This post is part of an ongoing series on governing the machine learning (ML) lifecycle at scale. To start from the beginning, refer to Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker.

article thumbnail

6 Killer Applications for Artificial Intelligence in the Customer Engagement Contact Center

If Artificial Intelligence for businesses is a red-hot topic in C-suites, AI for customer engagement and contact center customer service is white hot. This white paper covers specific areas in this domain that offer potential for transformational ROI, and a fast, zero-risk way to innovate with AI.

article thumbnail

Govern generative AI in the enterprise with Amazon SageMaker Canvas

AWS Machine Learning

This is crucial for compliance, security, and governance. In this post, we analyze strategies for governing access to Amazon Bedrock and SageMaker JumpStart models from within SageMaker Canvas using AWS Identity and Access Management (IAM) policies. We provide code examples tailored to common enterprise governance scenarios.

article thumbnail

Data Governance in the Age of AI: A Competitive Edge for Business Leaders

COPC

But here’s the reality: none of that happens without reliable data governance. However, the surge in AI adoption means governance frameworks must adapt to keep pace. Data governance is necessary to maintain these models’ reliability and meet internal and regulatory guidelines.