Open Source frameworks such as Spark, TensorFlow, MXNet, and PyTorch enable anyone to model and train deep learning models. While there are many great tutorials and talks showing us the best ways for training models, there is little information on what happens before and after we have trained our model. How can we develop, store, utilize, test, and refine it?
In this talk we look at the complete deep learning pipeline and answer questions such as:
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