AutoML in Microsoft Azure
AutoML is treated as a first-class citizen in the Azure platform. The fundamental ideas behind feature engineering, network architecture search, and hyperparameter tuning are the same as what we discussed in Chapter 2, Automated Machine Learning, Algorithms, and Techniques, and Chapter 3, Automated Machine Learning with Open Source Tools and Libraries. However, the layer of abstraction that's used to democratize these skills makes them much more appealing to non-machine learning experts.
The key principles of AutoML in the Azure platform are shown in the following diagram. User input such as datasets target metrics, and constraints (how long to run the job, what the allocated budget is for compute, and so on) drive the AutoML "engine", which completes iterations to find the best model and rank it according to the score of Training Success:
Figure 5.1 – Azure AutoML workflow – how AutoML works
In this...
