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Use Amazon SageMaker Model Card sharing to improve model governance

AWS Machine Learning

The framework that gives systematic visibility into ML model development, validation, and usage is called ML governance. During AWS re:Invent 2022, AWS introduced new ML governance tools for Amazon SageMaker which simplifies access control and enhances transparency over your ML projects.

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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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Alida gains deeper understanding of customer feedback with Amazon Bedrock

AWS Machine Learning

Alida helps the world’s biggest brands create highly engaged research communities to gather feedback that fuels better customer experiences and product innovation. Open-ended survey questions allow responders to provide context and unanticipated feedback. This post is co-written with Sherwin Chu from Alida.

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Use Amazon SageMaker Model Cards sharing to improve model governance

AWS Machine Learning

The framework that gives systematic visibility into ML model development, validation, and usage is called ML governance. During AWS re:Invent 2022, AWS introduced new ML governance tools for Amazon SageMaker which simplifies access control and enhances transparency over your ML projects.

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4 Reasons Why Live Chat for Government is Critical in 2022

Comm100

In US government, this score languishes at 4.5. For government organizations, this means reliance on the traditional channels of phone and email is no longer enough – live chat for government is essential. In this blog, we’ll look at the top five reasons why live chat for government is critical in 2022.

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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.

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Call Center Agent Feedback: Tips & Best Practices for Providing Effective Agent Feedback

Callminer

Providing feedback to agents in your call center is entirely needed to maintain and improve a quality facility. However, knowing how to deliver feedback can be tricky. Unfortunately, there are a number of pitfalls that can derail the process of delivering effective feedback.