AWS launches new SageMaker feature to make it easier to scale machine learning

AWS launches new SageMaker feature to make it easier to scale machine learning

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. Swami Sivasubramanian, the vice president of machine learning at Amazon, said the new features aim to make it easier for users to scale machine learning in their organizations.

Firstly, AWS launched a new SageMaker Ground Truth Plus service that uses an expert workforce to deliver highquality training datasets faster. SageMaker Ground Truth Plus uses a labeling workflow including machine learning techniques for active learning, prelabeling and machine validation. The company says the new service reduces costs by up to 40% and doesn`t require users to have deep machine learning expertise. The service enables users to create training datasets without having to build labeling applications. SageMaker Ground Truth Plus is currently available in Northern Virginia.

The company also rolled out a new SageMaker Inference Recommender tool to help users choose the best available compute instance to deploy machine learning models for optimal performance and cost. AWS says the tool automatically selects the right compute instance type, instance count, container parameters and model optimizations. Amazon SageMaker Inference Recommender is generally available in all regions where SageMaker is available except the AWS China regions.

In addition, AWS released the preview of a new SageMaker Serverless Interface option that allows users to easily deploy machine learning models for inference without having to configure or manage the underlying infrastructure. The new option is available in Northern Virginia, Ohio, Oregon, Ireland, Tokyo and Sydney.

AWS today released a new feature with SageMaker Training Compiler that allows you to more efficiently utilize GPU instances to accelerate  training of deep learning models by up to 50%. Its capabilities cover deep learning models, from high-level language representations to hardware optimization guidelines. New features are generally available in Northern Virginia, Ohio, Oregon, and Ireland.

Finally, AWS announced that users can now monitor and debug  Apache Spark jobs running on Amazon Elastic MapReduce (EMR) directly from their SageMaker Studio notebooks with  a single click. The company says you can now also discover, connect, create, disconnect, and manage EMR clusters directly from SageMaker Studio.

AWS explains in a blog post that "native EMR integration  enables  interactive petabyte-scale data preparation and machine learning in one general-purpose SageMaker Studio notebook."
The new SageMaker Studio feature is available in Northern Virginia, Ohio, Northern California, Oregon, Central Canada, Frankfurt, Ireland, Stockholm, Paris, London, Mumbai, Seoul, Singapore, Sydney, Tokyo and São Paulo. Regarding

AWS  today launched SageMaker Studio Lab, a free service that helps developers learn  and experiment with machine learning techniques. AWS yesterday announced a new machine learning service called Amazon SageMaker Canvas. The new service allows users to create machine learning predictive models using a point-and-click interface.