AWS Machine Learning
NOVEMBER 13, 2024
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.
AWS Machine Learning
NOVEMBER 14, 2024
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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AWS Machine Learning
OCTOBER 29, 2024
This post is part of an ongoing series on governing the machine learning (ML) lifecycle at scale. To start from the beginning, refer to Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker.
AWS Machine Learning
AUGUST 21, 2024
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.
AWS Machine Learning
AUGUST 31, 2023
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.
AWS Machine Learning
OCTOBER 20, 2023
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.
AWS Machine Learning
DECEMBER 1, 2022
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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