Summary
In this chapter, we covered the ML development life cycle and then defined automated ML and how it works. While building a case for the need for automated ML, we discussed the democratization of data science, debunked the myths surrounding automated ML, and provided a detailed walk-through of the automated ML ecosystem. Here, we reviewed the open source tools and then explored the commercial landscape. Finally, we discussed the future of automated ML, commented on the challenges and limitations of it, and finally provided some pointers on how to get started in an enterprise.
In the next chapter, we'll look under the hood of the technologies, techniques, and tools that are used to make automated ML possible. We hope that this chapter has introduced you to the automated ML fundamentals and that you are now ready to do a deeper dive into the topics that we discussed.
