  The rapidly developing AI systems and applications still require human
involvement in practically all parts of the analytics process. Human decisions
are largely based on visualizations, providing data scientists details of data
properties and the results of analytical procedures. Different visualizations
are used in the different steps of the Machine Learning (ML) process. The
decision which visualization to use depends on factors, such as the data
domain, the data model and the step in the ML process. In this chapter, we
describe the seven steps in the ML process and review different visualization
techniques that are relevant for the different steps for different types of
data, models and purposes.
