Summary
In this chapter, we introduced the concept of a data science workbench and explored some of the motivation behind adopting this tool as a way to accelerate our machine learning engineering practice.
We designed a data science workbench, using MLflow and adjacent technologies based on our requirements. We detailed the steps to set up your development environment with MLflow and illustrated how to use it with existing code. In later sections, we explored the workbench and added to it our stock-trading algorithm developed in the last chapter.
In the next chapter, we will focus on experimentation to improve our models with MLflow, using the workbench developed in this chapter.
