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From exploration to deployment - combining PyTorch and TensorFlow for Deep Learning


Despite the many deep learning frameworks out in the wild few have achieved widespread adoption. Two of them are TensorFlow and PyTorch. Where PyTorch relies on a dynamic computation graph TensorFlow goes for a static graph. Where TensorFlow shows greater adoption and additional useful extensions with TensorFlow Serving and TensorBoard, Pytorch proves useful trough its easy and more pythonic API.

Data scientists are confronted with explorative challenges, but also need to be aware of model deployment and production. Do we need to single out frameworks until we end up with the only one or is there a case for joint usage of two deep learning frameworks? Can we leverage the strengths of the frameworks for different tasks along the path from exploration to production?

In my talk, I want to present a case combining the benefits of PyTorch and TensorFlow using the first for explorative and latter for deployment tasks. Therefore, I will choose a common deep learning challenge and discuss the strengths and weaknesses of both frameworks along a demo that brings a model from development into production.

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