Why grabbing a model and playing won’t get you what you want

Machine learning should be approached as a science problem, not a software engineering problem. Before grabbing libraries like PyTorch or Keras and experimenting, developers need to understand the underlying math, hardware constraints, and data distribution principles. ML optimization requires a top-down approach: clarify the problem first, then determine whether you need fine-tuning, model alignment, data work, or hardware fixes. Fundamentals matter more than syntax — without understanding why models behave as they do, you'll get some results but not the ones you want.