Summary

ML is an integral part of any business strategy and decisions for many organizations today, thus it is very important to do it right. In this chapter, we learned about the general steps involved in a typical ML project development life cycle and their significance. We also highlighted some common challenges that ML practitioners face while undergoing project development. Finally, we listed some of the common limitations of ML in real-world scenarios to help us choose the right business problem and a fitting ML algorithm to solve it.

In this chapter, we learned about the importance of choosing the right business problem in order to deliver the maximum impact using ML. We also learned about the general flow of a typical ML project. We should now be confident about identifying the underlying ML-related challenges in a business process and making informed decisions about them. Finally, we have learned about the common limitations of ML algorithms, and it will help us apply ML in a better way to get the best out of it.

Just developing a high-performing ML model is not enough. The real value comes when it is deployed and used in real-world applications. Taking an ML model to production is not trivial and should be done in the right way. The next chapter is all about the guidelines and best practices to follow while operationalizing an ML model and it is going to be extremely important to understand it thoroughly before jumping into the later chapters of this book.