A standard ML workflow – Hands-On Python Deep Learning for the Web

A standard ML workflow

Any project starts with a problem in mind and ML projects are no exception. Before starting an ML project, it is very important to have a clear understanding of the problem that you are trying to solve using ML. Therefore, problem formulation and mapping with respect to the standard ML workflow serve as good starting points in an ML project. But what is meant by an ML workflow? This section is all about that. 

Designing ML systems and employing them to solve complex problems requires a set of skills other than just ML. It is good to know that ML requires knowledge of several things such as statistics, domain knowledge, software engineering, feature engineering, and basic high-school mathematics in varying proportions. To be able to design such systems, certain steps are fundamental to almost any ML workflow and each of these steps requires a certain skill set. In this section, we are going to take a look at these steps and discuss them briefly. 

This workflow is inspired by CRISP-DM, which stands for Cross Industry Standard Process for Data Mining and is extremely widely used across many industries pertaining to data mining and analytics.