Stages to Machine Learning

This article outlines a structured approach to machine learning projects, emphasizing the importance of starting with a clear business metric. It covers ten key stages: defining the business metric, blending data from multiple sources, exploring data through profiling and visualization, cleaning data to handle missing values and inconsistencies, and transforming data for consistency. The content is practical and focused on ensuring machine learning efforts are value-producing and measurable, targeting IT professionals and data scientists involved in ML workflows.