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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 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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Unlock the power of data governance and no-code machine learning with Amazon SageMaker Canvas and Amazon DataZone

AWS Machine Learning

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. However, ML governance plays a key role to make sure the data used in these models is accurate, secure, and reliable. For Select a data source , choose Athena.

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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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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 Deltek uses Amazon Bedrock for question and answering on government solicitation documents

AWS Machine Learning

This post provides an overview of a custom solution developed by the AWS Generative AI Innovation Center (GenAIIC) for Deltek , a globally recognized standard for project-based businesses in both government contracting and professional services. Deltek serves over 30,000 clients with industry-specific software and information solutions.

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How Human Centered Design Improves the Digital Contact Center Government Experience

Taylor Reach Group

The government will better serve all stakeholders by establishing a focus to oversee the design and implementation of a human-centered design strategy that: identifies and responds to key touch points in a stakeholder’s journey. Now when Jane talks about the government agency she shares her experience. By Rosetta Lue.