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Best practices for Amazon SageMaker HyperPod task governance

AWS Machine Learning

Administrators can use SageMaker HyperPod task governance to govern allocation of accelerated compute to teams and projects, and enforce policies that determine the priorities across different types of tasks. We also discuss common governance scenarios when administering and running generative AI development tasks.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning

We also dive deeper into access patterns, governance, responsible AI, observability, and common solution designs like Retrieval Augmented Generation. Model monitoring – The model monitoring service allows tenants to evaluate model performance against predefined metrics.

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Principal Financial Group uses QnABot on AWS and Amazon Q Business to enhance workforce productivity with generative AI

AWS Machine Learning

Principal implemented several measures to improve the security, governance, and performance of its conversational AI platform. Additional integrations with services like Amazon Data Firehose , AWS Glue , and Amazon Athena allowed for historical reporting, user activity analytics, and sentiment trends over time through Amazon QuickSight.

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Unlock the power of data governance and no-code machine learning with Amazon SageMaker Canvas and Amazon DataZone

AWS Machine Learning

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. However, ML governance plays a key role to make sure the data used in these models is accurate, secure, and reliable. For Select a data source , choose Athena.

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

SageMaker is a data, analytics, and AI/ML platform, which we will use in conjunction with FMEval to streamline the evaluation process. Evaluation algorithm Computes evaluation metrics to model outputs. Different algorithms have different metrics to be specified. We specifically focus on SageMaker with MLflow.

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How to Successfully Implement Customer Journey Analytics – Part 1

Pointillist

Companies are increasingly benefiting from customer journey analytics across marketing and customer experience, as the results are real, immediate and have a lasting effect. Learning how to choose the best customer journey analytics platform is just the start. Steps to Implement Customer Journey Analytics. By Swati Sahai.

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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. Meanwhile, active data enables agility.