Meta will use AWS services and global infrastructure to scale research and development, facilitate third party collaborations, and drive operational efficiency
Meta and AWS will jointly help enterprises use PyTorch on AWS to bring deep learning models from research into production faster and easier
SEATTLE–(BUSINESS WIRE)–Today, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), announced that Meta (NASDAQ: FB) has deepened its relationship with AWS as a strategic cloud provider. Meta uses AWS’s proven infrastructure and comprehensive capabilities to complement its existing on-premises infrastructure, and will broaden its use of AWS compute, storage, databases, and security services to provide privacy, reliability, and scale in the cloud. Meta will run third-party collaborations in AWS and use the cloud to support acquisitions of companies that are already powered by AWS. It will also use AWS’s compute services to accelerate artificial intelligence (AI) research and development for its Meta AI group. In addition, Meta and AWS will work together to improve the performance for customers running PyTorch on AWS and accelerate how developers build, train, deploy, and operate artificial intelligence/machine learning models.
AWS and Meta will help machine learning researchers and developers by further optimizing PyTorch performance and its integration with core managed services such as Amazon Elastic Compute Cloud (Amazon EC2) and Amazon SageMaker (AWS’s service that helps developers and data scientists build, train, and deploy machine learning models quickly in the cloud and at the edge) for building, training, and deploying artificial intelligence models at scale. To make it easier for developers to build large-scale deep learning models for natural language processing and computer vision, the companies are enabling PyTorch on AWS to orchestrate large-scale training jobs across a distributed system of AI accelerators. The companies will work together to offer native tools to improve the performance, explainability, and cost of inference on PyTorch. To simplify the deployment of models in production, the companies will continue to enhance TorchServe, the serving engine native to PyTorch that makes it easy to deploy trained PyTorch models at scale. Building on these open-source contributions, AWS and Meta plan to help organizations bring large-scale deep learning models from research to production faster and easier with optimized performance on AWS.
“Meta and AWS have been expanding our collaboration over the last five years,” said Kathrin Renz, Vice President of Business Development and Industries at Amazon Web Services, Inc. “With this agreement, AWS will continue to help Meta support research and development, drive innovation, and collaborate with third parties and the open-source community at scale. Customers can rely on Meta and AWS to collaborate on PyTorch, making it easier for them to build, train, and deploy deep learning models on AWS.”
“We are excited to extend our strategic relationship with AWS to help us innovate faster and expand the scale and scope of our research and development work,” said Jason Kalich, Vice President of Production Engineering at Meta. “The global reach and reliability of AWS will help us continue to deliver innovative experiences for the billions of people around the world that use Meta products and services and for customers running PyTorch on AWS.”
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