Summary

This chapter was quite intensive in terms of hands-on examples and demonstrations. You've hopefully managed to learn how to train automated classification pipelines with TPOT and what you can tweak during the process.

You should now be capable of training any kind of automated machine learning model with TPOT, whether we're talking about regression, classification, standard classifiers, or neural network classifiers. There is good news, as this was the last chapter with TPOT examples.

In the following chapter, Chapter 8, TPOT Model Deployment, you'll learn how to wrap the predictive functionality of your models inside a REST API, which will then be tested and deployed both locally and to the cloud. You'll also learn how to communicate with the API once it's deployed.

Finally, in the previous chapter, Chapter 9, Using the Deployed TPOT Model in Production, you'll learn how to develop something useful with the deployed APIs. To be more precise...