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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.

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Optimize your machine learning deployments with auto scaling on Amazon SageMaker

AWS Machine Learning Blog

SageMaker supports automatic scaling (auto scaling) for your hosted models. Auto scaling dynamically adjusts the number of instances provisioned for a model in response to changes in your inference workload. When the workload increases, auto scaling brings more instances online.

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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. Is it accessible from your language/framework/infrastructure, framework, or infrastructure?

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Google’s Dr. Arsanjani on Enterprise Foundation Model Challenges

Snorkel AI

From a software engineering perspective, machine-learning models, if you look at it in terms of the number of parameters and in terms of size, started out from the transformer models. So the application started to go from the pure software-engineering/machine-learning domain to industry and the sciences, essentially.

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Google’s Arsanjani on Enterprise Foundation Model Challenges

Snorkel AI

From a software engineering perspective, machine-learning models, if you look at it in terms of the number of parameters and in terms of size, started out from the transformer models. So the application started to go from the pure software-engineering/machine-learning domain to industry and the sciences, essentially.