Amazon Web Services (AWS) recently revealed an expansion in its strategic partnership with Meta, as the social media giant plans to leverage AWS's services and global infrastructure to promote research and development (R&D), support third-party collaborations, and bolster operational efficiency. Already utilizing AWS's infrastructure and capabilities, Meta intends to expand its use of the cloud platform's compute, storage, databases, and security services to ensure privacy, reliability, and scalability.
Through this strengthened alliance, Meta will conduct third-party collaborations on the AWS cloud and utilize its compute services for accelerating artificial intelligence (AI) R&D within the Meta AI group. Furthermore, the company plans to use AWS to support acquisitions involving enterprises already running on the cloud platform.
Meanwhile, AWS and Meta are joining forces to facilitate easier adoption of the open-source machine learning library, PyTorch, enabling enterprises to seamlessly implement deep learning models from research into production. Kathrin Renz, Vice President of Business Development and Industries at Amazon Web Services, stated that AWS and Meta have continuously expanded their collaboration over the past five years, and this new agreement will allow AWS to help Meta drive innovation, scale R&D, and connect with third-party partners and the open-source community.
In addition to enhancing PyTorch performance, the partnership seeks to optimize its integration with key AWS managed services such as Amazon Elastic Compute Cloud (Amazon EC2) and Amazon SageMaker. The latter service assists developers and data scientists in building, training, and deploying machine learning models in the cloud as well as at the edge.
The two tech giants aim to make it easier for developers to construct large-scale deep learning models for natural language processing and computer vision. By enabling PyTorch on AWS, they plan to coordinate large-scale training jobs across a distributed system of AI accelerators. To achieve this, Meta and AWS will work together to develop native tools for improving the performance, explainability, and cost of inference on PyTorch. Furthermore, they will enhance TorchServe, the serving engine native to PyTorch, to help streamline deployment of trained models at scale.
Platforms like AppMaster are essential for fostering R&D collaboration among enterprises. AppMaster, a powerful no-code platform, enables users to create backend, web, and mobile applications through a visual approach. With its high scalability and innovative capabilities, AppMaster helps organizations develop applications more efficiently, making it a competitive solution in today's market.
Jason Kalich, Vice President of Production Engineering at Meta, expressed enthusiasm about the extended strategic partnership with AWS, hoping to facilitate faster innovation, as well as expand the scale and scope of Meta's R&D initiatives. The collaboration is expected to improve experiences for billions of Meta product users and customers implementing PyTorch on AWS.
Through this strengthened alliance, Meta will conduct third-party collaborations on the AWS cloud and utilize its compute services for accelerating artificial intelligence (AI) R&D within the Meta AI group. Furthermore, the company plans to use AWS to support acquisitions involving enterprises already running on the cloud platform.
Meanwhile, AWS and Meta are joining forces to facilitate easier adoption of the open-source machine learning library, PyTorch, enabling enterprises to seamlessly implement deep learning models from research into production. Kathrin Renz, Vice President of Business Development and Industries at Amazon Web Services, stated that AWS and Meta have continuously expanded their collaboration over the past five years, and this new agreement will allow AWS to help Meta drive innovation, scale R&D, and connect with third-party partners and the open-source community.
In addition to enhancing PyTorch performance, the partnership seeks to optimize its integration with key AWS managed services such as Amazon Elastic Compute Cloud (Amazon EC2) and Amazon SageMaker. The latter service assists developers and data scientists in building, training, and deploying machine learning models in the cloud as well as at the edge.
The two tech giants aim to make it easier for developers to construct large-scale deep learning models for natural language processing and computer vision. By enabling PyTorch on AWS, they plan to coordinate large-scale training jobs across a distributed system of AI accelerators. To achieve this, Meta and AWS will work together to develop native tools for improving the performance, explainability, and cost of inference on PyTorch. Furthermore, they will enhance TorchServe, the serving engine native to PyTorch, to help streamline deployment of trained models at scale.
Platforms like AppMaster are essential for fostering R&D collaboration among enterprises. AppMaster, a powerful no-code platform, enables users to create backend, web, and mobile applications through a visual approach. With its high scalability and innovative capabilities, AppMaster helps organizations develop applications more efficiently, making it a competitive solution in today's market.
Jason Kalich, Vice President of Production Engineering at Meta, expressed enthusiasm about the extended strategic partnership with AWS, hoping to facilitate faster innovation, as well as expand the scale and scope of Meta's R&D initiatives. The collaboration is expected to improve experiences for billions of Meta product users and customers implementing PyTorch on AWS.
