Why enterprises are turning from TensorFlow to PyTorch

A subcategory of machine learning, deep learning uses multi-layered neural networks to automate historically difficult machine tasks—such as image recognition, natural language processing (NLP), and machine translation—at scale.

TensorFlow, which emerged out of Google in 2015, has been the most popular open source deep learning framework for both research and business. But PyTorch, which emerged out of Facebook in 2016, has quickly caught up, thanks to community-driven improvements in ease of use and deployment for a widening range of use cases.

PyTorch is seeing particularly strong adoption in the automotive industry—where it can be applied to pilot autonomous driving systems from the likes of Tesla and Lyft Level 5. The framework also is being used for content classification and recommendation in media companies and to help support robots in industrial applications.

Joe Spisak, product lead for artificial intelligence at Facebook AI, told InfoWorld that although he has been pleased by the increase in enterprise adoption of PyTorch, there’s still much work to be done to gain wider industry adoption.

“The next wave of adoption will come with enabling lifecycle management, MLOps, and Kubeflow pipelines and the community around that,” he said. “For those early in the journey, the tools are pretty good, using managed services and some open source with something like SageMaker at AWS or Azure ML to get started.”

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Copyright © 2020 IDG Communications, Inc.

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