Summary

In this chapter, we have introduced the idea of ML engineering and how that fits within a modern team building valuable solutions based on data. There was a discussion of how the focus of ML engineering is complementary to the strengths of data science and data engineering and where these disciplines overlap. Some comments were made about how to use this information to assemble an appropriately resourced team for your projects.

The challenges of building machine learning products in modern real-world organizations were then discussed, along with pointers to help you overcome some of these challenges. In particular, the notion of reasonably estimating value and effectively communicating with your stakeholders were emphasized.

This chapter then rounded off with a taster of the technical content to come in later chapters, in particular, through a discussion of what typical ML solutions look like and how they should be designed (at a high level) for some common use cases.

The next chapter will focus on how to set up and implement your development processes to build the ML solutions you want and provide some insight as to how this is different from standard software development processes. Then there will be a discussion of some of the tools you can use to start managing the tasks and artifacts from your projects without creating major headaches. This will set you up for the technical details of how to build the key elements of your ML solutions in later chapters.