The Fundamental Differences Between ML Model Development and Traditional Enterprise Software Development - DZone AI

Academic literature on machine learning modeling does not explicitly address how enterprises across industries can utilize ML algorithms. And many companies, even after investing in foundational ML tools, still often get puzzled when defining business use cases for their AI apps, customizing general purpose machine learning models for domain-specific tasks, converting business requirements into data requirements, etc. In this post, we'll talk about key differences between traditional enterprise software development and ML model building and offer some ML lifecycle management tips (chiefly concerning data preparation and feature engineering) for those seeking to harness AI. In traditional software development we write out explicit instructions for a computer to follow and, therefore, the applications we end up with are deterministic. In machine learning, which is probabilistic in nature, we rely on data to write our if-then statements.