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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.