The CRISP-DM model provides a high-level workflow for management of ML and related projects. In this section, we will discuss the technical aspects and implementation of standard workflows for handling ML projects. Simply put, an ML pipeline is an end-to-end workflow consisting of various aspects of a data intensive project. Once the initial phases such as business understanding, risk assessments, and ML or data mining techniques selection have been covered, we proceed towards the solution space of driving the project. A typical ML pipeline or workflow with different sub-components is shown in the following diagram:

A standard ML pipeline broadly consists of the following stages.