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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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How will COVID-19 change customer behavior and habits?

Beyond Philosophy

One of the airlines I had bookings with had put a note for a number of days saying they are working on a self-service option to change tickets with open ones via their website or app. The response he received after complaining was even more disappointing. Digital Transformation. Web Meetings and Working from Home (WFH).

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Transforming citizen experience: How AI and automation can revolutionize public services

3CLogic

Whether citizens are applying for permits, accessing healthcare services, or resolving tax inquiries, expectations have shifted. Yet, government agencies still face challenges such as outdated technology, siloed systems, data privacy concerns, and a shrinking workforce. The result?

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Enabling generative AI self-service using Amazon Lex, Amazon Bedrock, and ServiceNow

AWS Machine Learning

With its robust capabilities for ticketing, knowledge management, human resources (HR) services, and more, ServiceNow is already powering many enterprise service desks. In this post, we demonstrate how to integrate Amazon Lex with Amazon Bedrock Knowledge Bases and ServiceNow.

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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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How to Make Your Experience Easy and Gain Growth

Beyond Philosophy

The automatic and intuitive side of our decision-making governs habitual behavior. However, you drive that route so often, it’s automatic and handled by your cognitive auto-pilot, habit, which is governed by the Intuitive System of your thinking. . The automatic and intuitive side of our decision-making governs habitual behavior.

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

AWS Machine Learning

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. However, implementing security, data privacy, and governance controls are still key challenges faced by customers when implementing ML workloads at scale.