Broadcast Date: February 24, 2023
Level: 300
As companies are increasingly adopting ML in mainstream enterprise applications, they require control over and visibility into their ML projects, also known as ML governance. Amazon SageMaker recently launched new capabilities to help customers define user permissions, and create a single source of truth for model information and gain insights into model performance and troubleshoot deviations from a single view. Join us for a deep dive into Amazon SageMaker Role Manager, SageMaker Model Cards, and SageMaker Model Dashboard.
Learning Objectives
- Define custom permissions with Amazon SageMaker Role Manager.
- Create a single source of truth for model information with Amazon SageMaker Model Cards.
- Audit and troubleshoot all your models through a single view using Amazon SageMaker Model Dashboard.
Who Should Attend?
Data Scientists, Developers, ML Practitioners, ML Platform Administrators, ML Administrators
Speaker(s)
Ozan Eken, Senior Product Manager, AWS AI Platforms; Ram Vittal, Principal Solution Architect, AWS WWSO AI/ML
Learn More
To learn more about the services featured in this talk, please visit:
aws.amazon.com/sagemaker/ml-governance/
Intro body copy here about 2018 re:Invent launches.
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