Summary
This chapter was packed with information not only about TPOT and training models in a parallel manner, but also about parallelism in general. You've learned a lot – from how to parallelize basic functions that do nothing but sleep for a while, to parallelizing function with parameters and Dask fundamentals, to training machine learning models with TPOT and Dask on a Dask cluster.
By now, you know how to solve regression and classification tasks in an automated manner, and how to parallelize the training process. The following chapter, Chapter 6, Getting Started with Deep Learning – Crash Course in Neural Networks, will provide you with the required knowledge on neural networks. It will form a basis for Chapter 7, Neural Network Classifier with TPOT, where we'll dive deep into training automated machine learning models with state-of-the-art neural network algorithms.
As always, please feel free to practice solving both regression and classification...
