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How Government (and Any Business) Can Transform Customer Experience with Gabriele Masili

ShepHyken

Trust is essential when dealing with government services. When people trust their government, they have better experiences. When people feel understood and valued, their trust in the service provider, whether the government or private companies, grows. Plus, G shares how staffing issues affect government service delivery.

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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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Governing ML lifecycle at scale: Best practices to set up cost and usage visibility of ML workloads in multi-account environments

AWS Machine Learning

This post outlines steps you can take to implement a comprehensive tagging governance strategy across accounts, using AWS tools and services that provide visibility and control. Tagging is an effective scaling mechanism for implementing cloud management and governance strategies.

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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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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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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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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.