Summary

In this chapter, we learned when and how to use DL to train an ML model on Azure. We used both compute instance and a GPU cluster from within Azure Machine Learning to train a model using Keras and TensorFlow.

First, we found out that DL works very well on highly structured data with non-obvious relations from the raw input data to the resulting prediction. Good examples are image classification, speech-to-text, or translation. However, we also saw that DL models are parametric models with a large number of parameters and so we often need a large amount of labeled or augmented input data. In contrast to traditional ML approaches, the extra parameters are used to train a fully end-to-end model, also including feature extraction from the raw input data.

Training a CNN using Azure Machine Learning is not difficult. We saw many approaches, from prototyping in Jupyter to augmenting the training data to running the training on a GPU cluster with autoscaling. The difficult part...