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AWS Announces Eight New Amazon SageMaker Capabilities

At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), today announced eight new capabilities for Amazon SageMaker, its end-to-end machine learning (ML) service. Developers, data scientists, and business analysts use Amazon SageMaker to build, train, and deploy ML models quickly and easily using its fully managed infrastructure, tools, and workflows. As customers continue to innovate using ML, they are creating more models than ever before and need advanced capabilities to efficiently manage model development, usage, and performance. Today’s announcement includes new Amazon SageMaker governance capabilities that provide visibility into model performance throughout the ML lifecycle. New Amazon SageMaker Studio Notebook capabilities provide an enhanced notebook experience that enables customers to inspect and address data-quality issues in just a few clicks, facilitate real-time collaboration across data science teams, and accelerate the process of going from experimentation to production by converting notebook code into automated jobs. Finally, new capabilities within Amazon SageMaker automate model validation and make it easier to work with geospatial data. To get started with Amazon SageMaker, visit aws.amazon.com/sagemaker.

“Today, tens of thousands of customers of all sizes and across industries rely on Amazon SageMaker. AWS customers are building millions of models, training models with billions of parameters, and generating trillions of predictions every month. Many customers are using ML at a scale that was unheard of just a few years ago,” said Bratin Saha, vice president of Artificial Intelligence and Machine Learning at AWS. “The new Amazon SageMaker capabilities announced today make it even easier for teams to expedite the end-to-end development and deployment of ML models. From purpose-built governance tools to a next-generation notebook experience and streamlined model testing to enhanced support for geospatial data, we are building on Amazon SageMaker’s success to help customers take advantage of ML at scale.”

The cloud enabled access to ML for more users, but until a few years ago, the process of building, training, and deploying models remained painstaking and tedious, requiring continuous iteration by small teams of data scientists for weeks or months before a model was production-ready. Amazon SageMaker launched five years ago to address these challenges, and since then AWS has added more than 250 new features and capabilities to make it easier for customers to use ML across their businesses. Today, some customers employ hundreds of practitioners who use Amazon SageMaker to make predictions that help solve the toughest challenges around improving customer experience, optimizing business processes, and accelerating the development of new products and services. As ML adoption has increased, so have the types of data that customers want to use, as well as the levels of governance, automation, and quality assurance that customers need to support the responsible use of ML. Today's announcement builds on Amazon SageMaker's history of innovation in supporting practitioners of all skill levels, worldwide.

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AWS Announces Eight New Amazon SageMaker Capabilities At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), today announced eight new capabilities for Amazon SageMaker, its end-to-end machine learning (ML) service. Developers, data scientists, and business …

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