Why companies are moving from TensorFlow to PyTorch

 A subcategory of machine learning, deep learning (also known as deep learning) uses multi-layered neural networks to scale up tasks that are still quite difficult for machines, such as image recognition, natural language processing (NPL) and automatic translation. 


The TensorFlow , who came from Google in 2015, has been the deep learning framework most popular open source research and business. But PyTorch , which emerged from Facebook in 2016, quickly achieved this solution, thanks to improvements made by the community in ease of use and deployment for an ever- expanding range of use cases. 


PyTorch is having a particularly strong adoption in the automotive industry - where it can be applied in autonomous steering systems from companies like Tesla and Lyft, at level 5 of automotive autonomy. The framework is also being used for content rating and recommendation in media companies and to assist support robots in industrial applications. 


Joe Spisak, artificial intelligence product leader at Facebook AI , told InfoWorld that, while he was pleased with the increase in PyTorch adoption by companies, there is still a lot of work to be done to achieve broader industry adoption. 

A Microsoft Certified IT Professional (MCITP) Database Administrator installs and maintains Microsoft SQL Server, multidimensional databases and user accounts as well as ensures database availability, recovery, and reporting.

"The next wave of adoption will come with enabling product lifecycle management, improvements to MLOps and Kubeflow pipelines , and [community participation] around that," he said. "For those at the beginning of the journey, the tools are very good, using managed and open source services such as SageMaker the AWS or Azure ML to start." 

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