The Harry Potter Problem: Define the Why to Succeed With Your Machine Learning Project

The success of any machine learning project starts with defining why you're doing it. Side projects: The next way to trip up is to define a machine learning project as a side project. Yet another promising machine learning project was refused because the site data specialist wanted to understand how the artificial intelligence algorithms worked and the supplier did not want to answer his request, for secrecy and competitive reasons. So, while the data scientists and process engineers I mentioned in the first article are required, they don't have the right profiles to drive these types of fundamental issues, the questions about the why.