At its annual re:Invent conference, AWS today rolled out a slew of new features for SageMaker, the company’s managed service for building, training and deploying machine learning (ML) models.

He says that a data scientist starts by uploading an exported model file to S3 cloud storage. “Then we pull it, containerize it and deploy it on Kubernetes behind the scenes.

AWS says it can speed up training by up to 50%. Next, Amazon SageMaker Inference Recommender automates load testing and optimizes model performance across machine learning instances.

Machine learning is an iterative process that requires teams to build, train, and deploy applications quickly, as well as train, retrain, and experiment frequently to increase the prediction ...

While large-scale machine learning models are very useful, setting up and running them requires special expertise that few companies possess. The new partnership between Hugging Face and AWS will ...

At re:Invent 2022, the cloud services provider updated its managed machine learning service to include new notebook and governance features.

The interface gives developers a place where they can prototype, build, train, and deploy machine learning models for cloud and mobile apps. Written by Natalie Gagliordi, Contributor Oct. 12, 2017 ...