Amazon SageMaker continues to expand machine learning (ML) use in the cloud

by | Oct 28, 2022 | Technology

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Amazon SageMaker, which got its start five years ago, is among the most widely used machine learning (ML) services in existence.  

Back in 2017 Sagemaker was a single service designed to help organizations use the cloud to train ML models. Much like how Amazon Web Services (AWS) has grown significantly over the last five years, so too has the number of ML services under the Sagemaker portfolio. 

In 2018, Amazon SageMaker GroundTruth added data labeling capabilities. In 2019, AWS expanded SageMaker with a number of services including SageMaker Studio, which provides an integrated developer environment (IDE) for data scientists to build ML application workflows. The SageMaker Data Wrangler service was announced in 2020 for data preparation and in 2021 new capabilities included the Clarify explainability and ML feature store services.

AWS is continuing to add services to SageMaker, including a pair of announcements made yesterday, with new support for AWS Graviton cloud instances and multi-model endpoint support. During an AWS event on Oct. 26, Bratin Saha, VP and general manager of AI/ML at AWS, said there are over 100,000 customers from virtually every industry who make use of AWS’s cloud ML services.

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“Machine learning isn’t the future that we need to plan for, it’s the present that we need to harness now,” Bratin said.

AWS scales SageMaker with multi-model endpoints (MME)

One of the things that has happened over the last five years of SageMaker adoption is an increase in scale for how models are trained and deployed.

To help organizations deal with the challenge of scaling, Bratin said that AWS has released the SageMaker multi-model endpoints (MME) capability.

“This allows a single GPU to host thousands of models,” Bratin said. “Many of the most common use cases for machine learning, such as personalization, require you to mana …

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