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AWS launches new SageMaker features to help scale machine learning – TechCrunch

At its annual re: Invent conference, AWS today rolled out a host of new features for SageMaker, the company’s managed service for building, training, and deploying machine learning (ML) models. Swami Sivasubramanian, vice president of machine learning at Amazon, said the new features are aimed at making it easier for users to scale machine learning in their organizations.

First, AWS launched a new SageMaker Ground Truth Plus service that uses an expert workforce to deliver high-quality training datasets faster. SageMaker Ground Truth Plus uses a labeling workflow that includes machine learning techniques for active learning, pre-labeling, and automatic validation. The company says the new service cuts costs by up to 40% and doesn’t require users to have deep machine learning expertise. The service allows users to create training datasets without having to create labeling applications. SageMaker Ground Truth Plus is currently available in Northern Virginia.

The company also deployed a new SageMaker inference recommendation tool to help users choose the best compute instance available to deploy machine learning models for optimal performance and cost. AWS indicates that the tool automatically selects the correct type of compute instance, number of instances, container settings, and model optimizations. The Amazon SageMaker Inference Recommendation Tool is generally available in all regions where SageMaker is available except AWS China Regions.

Additionally, AWS has previewed 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 launches new SageMaker features to help scale machine learning – TechCrunch

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With SageMaker Training Compiler, AWS today released a new feature that can accelerate the training of deep learning models by up to 50% through more efficient use of GPU instances. The functionality covers deep learning models, from their high-level linguistic representation to hardware-optimized instructions. The new feature is generally available in Northern Virginia, Ohio, Oregon, and Ireland.

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

“The integrated integration with EMR therefore allows you to perform interactive data preparation and machine learning at the petabyte scale directly in the only universal notebook SageMaker Studio,” AWS explains in a blog post.

New SageMaker Studio features are available in Northern Virginia, Ohio, Northern California, Oregon, Central Canada, Frankfurt, Ireland, Stockholm, Paris, London, Mumbai, Seoul, Singapore, Sydney, Tokyo, and Sao Paolo.

On a related note, AWS today also launched SageMaker Studio Lab, a free service to help developers learn machine learning techniques and experience the technology. Yesterday, AWS announced a new machine learning service called Amazon SageMaker Canvas. The new service will allow users to create machine learning prediction models, using a point-and-click interface.

AWS launches new SageMaker features to help scale machine learning – TechCrunch

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