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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.

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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.

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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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Boost post-call analytics with Amazon Q in QuickSight

AWS Machine Learning

Post Call Analytics (PCA) is a solution that does most of the heavy lifting associated with providing an end-to-end solution that can process call recordings from your existing contact center. These capabilities can significantly enhance your post-call analytics workflow, making it easier to derive insights from your contact center data.

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B2B Customer Experience Governance

ClearAction

B2B Customer Experience Governance Lynn Hunsaker B2B customer experience governance can generate stronger growth when it’s tied-in to the way that B2B ecosystems work. Governance of any endeavor is strongest when it’s integrated as your company’s way of life. Built-in B2B Customer Experience Governance 1.

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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning

However, scaling up generative AI and making adoption easier for different lines of businesses (LOBs) comes with challenges around making sure data privacy and security, legal, compliance, and operational complexities are governed on an organizational level. In this post, we discuss how to address these challenges holistically.

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Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator

AWS Machine Learning

Data and model management provide a central capability that governs ML artifacts throughout their lifecycle. Integrations with CI/CD workflows and data versioning promote MLOps best practices such as governance and monitoring for iterative development and data versioning. It enables auditability, traceability, and compliance.

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