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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

Use case and model governance plays a crucial role in implementing responsible AI and helps with the reliability, fairness, compliance, and risk management of ML models across use cases in the organization. Following are the steps completed by using APIs to create and share a model package group across accounts.

ML 89
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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Can you see the complete model lineage with data/models/experiments used downstream? Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on. MLOps workflows for computer vision and ML teams Use-case-centric annotations.

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Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – part 1

AWS Machine Learning Blog

This dramatic improvement in loading speed opens up new possibilities for responsive AI systems, potentially enabling faster scaling and more dynamic applications that can adapt quickly to changing demands. For more details, see Amazon SageMaker inference launches faster auto scaling for generative AI models and Container Caching.