In this chapter, we have explored the common pitfalls of data science projects, as well as how to manage research projects with tools such as experiment backlog and experiment tracking. We have also seen how prototypes differ from research projects and looked at how to manage prototype development from the standpoint of an MVP. Those techniques were then summarized in a case study that concerned MVP development in a consulting company. Finally, we enumerated and systematized the major risks and their solutions for research, prototype, and production systems.
In the next chapter, we will look at how to grow data science products and improve internal team performance by using reusable technology.
