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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. However, as data volumes and complexity continue to grow, effective data governance becomes a critical challenge.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

This streamlines the ML workflows, enables better visibility and governance, and accelerates the adoption of ML models across the organization. Before we dive into the details of the architecture for sharing models, let’s review what use case and model governance is and why it’s needed.

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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.

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What is PCI Compliance Call Recording & Transcription: Definition, Expert Tips & Best Practices

Callminer

Perhaps the strongest reason companies record and/or transcribe calls is that it’s often required by government entities. Expert PCI Compliance Tips & Best Practices. Below, we’ve rounded up 17 tips and best practices for PCI compliance from industry and regulatory experts. Expand your call recording practices.

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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.

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

AWS Machine Learning

Amazon Bedrock empowers teams to generate Terraform and CloudFormation scripts that are custom fitted to organizational needs while seamlessly integrating compliance and security best practices. This makes sure your cloud foundation is built according to AWS best practices from the start.

Scripts 132
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Best practices to build generative AI applications on AWS

AWS Machine Learning

Safety – Retrieving the information from required and permitted data sources can improve governance and control over harmful and inaccurate content generation. For more details about training LLMs on SageMaker, refer to Training large language models on Amazon SageMaker: Best practices and SageMaker HyperPod.