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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. Regular evaluations allow you to adjust and steer the AI’s behavior based on feedback and performance metrics.

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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. In this second part, we expand the solution and show to further accelerate innovation by centralizing common Generative AI components.

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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.

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Generative AI operating models in enterprise organizations with Amazon Bedrock

AWS Machine Learning

Large organizations often have many business units with multiple lines of business (LOBs), with a central governing entity, and typically use AWS Organizations with an Amazon Web Services (AWS) multi-account strategy. LOBs have autonomy over their AI workflows, models, and data within their respective AWS accounts.

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Accountability in the Contact Center

Contact Center Pipeline

“We want to make people more accountable.” As a concept, accountability has enormous appeal. It is discussed in relation to government, education, non-profits and every corner of the business sector. An increase in accountability, done properly, is welcomed by executives, management and engaged staff at […].

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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.

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Improve governance of your machine learning models with Amazon SageMaker

AWS Machine Learning

Overview of model governance. Model governance is a framework that gives systematic visibility into model development, validation, and usage. Model governance is applicable across the end-to-end ML workflow, starting from identifying the ML use case to ongoing monitoring of a deployed model through alerts, reports, and dashboards.