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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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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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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. Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services.

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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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Insights in implementing production-ready solutions with generative AI

AWS Machine Learning

Achieving scale, reliability, and compliance Factors to consider in transitioning to full-scale production include scalability, data governance, privacy, consistent and responsible AI behaviors, security, integration with existing systems, monitoring, end-user feedback collection, and business impact measurement.

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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning

However, scaling up generative AI and making adoption easier for different lines of businesses (LOBs) comes with challenges around making sure data privacy and security, legal, compliance, and operational complexities are governed on an organizational level. In this post, we discuss how to address these challenges holistically.

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Generate financial industry-specific insights using generative AI and in-context fine-tuning

AWS Machine Learning

Bond types**: The list covers a range of bond types, including corporate bonds, government bonds, high-yield bonds, and green bonds. In entered the Big Data space in 2013 and continues to explore that area. He is focused on Big Data, Data Lakes, Streaming and batch Analytics services and generative AI technologies.