Chapter 3: Your Data Science Workbench
In this chapter, you will learn about MLflow in the context of creating a local environment so that you can develop your machine learning project locally with the different features provided by MLflow. This chapter is focused on machine learning engineering, and one of the most important roles of a machine learning engineer is to build up an environment where model developers and practitioners can be efficient. We will also demonstrate a hands-on example of how we can use workbenches to accomplish specific tasks.
Specifically, we will look at the following topics in this chapter:
- Understanding the value of a data science workbench
- Creating your own data science workbench
- Using the workbench for stock prediction
