The Machine Learning Architect's Guide: Selecting and Combining Learning Paradigms
Navigating the machine learning landscape can feel like being a chef in a world-class kitchen. You have every ingredient and tool imaginable, but the success of the meal hinges on using the right technique at the right time. Should you sauté, braise, or grill? In ML, the question is: do you use supervised, unsupervised, reinforcement, or—increasingly—a clever fusion of them all? This isn’t just an academic exercise. Choosing the wrong paradigm is the quickest path to inflated costs, missed deadlines, and a model that simply doesn’t work. This guide goes beyond definitions. We'll explore the strategic reasoning behind each approach, dive into the powerful synergies of hybrid models, and equip you with the architectural mindset needed to blueprint your next project for success. a. The Evolution of Learning: From Rule-Based Systems to Contextual Intelligence The story of ML is one of increasing abstraction and autonomy. Early AI was dominated by rule-based systems , where humans had ...