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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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6 Reasons Why Data Governance Services Are Essential for Fraud Prevention in Finance

CSM Magazine

In this high-stakes environment, data governance services stand out as a vital pillar of protection. By ensuring data accuracy, integrity, and proper stewardship, data governance frameworks enable organizations to detect and prevent fraudulent activities before they spiral out of control.

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Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

AWS Machine Learning

However, implementing security, data privacy, and governance controls are still key challenges faced by customers when implementing ML workloads at scale. Governing ML lifecycle at scale is a framework to help you build an ML platform with embedded security and governance controls based on industry best practices and enterprise standards.

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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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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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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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Building a Great CX Team

CX Accelerator

Customer Insights/Measurement/Analytics. CUSTOMER INSIGHTS/MEASUREMENT/ANALYTICS Understanding your customers is at the heart of customer experience. Once customer data has been gathered, an analytics function is required to derive meaningful, actionable insight from it. The 8 skills required by any CX team are: Strategy.