Summary
In this chapter, we have learned about the ML solution development process, how to identify a suitable ML solution to a problem, and how to categorize operations to implement suitable MLOps. We got a glimpse into a generic implementation roadmap and saw some tips for procuring essentials such as tools, data, and infrastructure to implement your ML application. Lastly, we went through the business problem to be solved in the next chapter by implementing an MLOps workflow (discussed in Chapter 1, Fundamentals of MLOps Workflow) in which we'll get some hands-on experience in MLOps.
In the next chapter, we will go from theory to practical implementation. The chapter gets hands-on when we start with setting up MLOps tools on Azure and start coding to clean the data to address the business problem and get plenty of hands-on experience.
