For years, the workflow for Android developers looking to implement on-device Machine Learning (ML) followed a predictable, albeit exhausting, pattern. You would download a .tflite model, drop it into your assets folder, and prepare for a long weekend of writing boilerplate. You had to manually handle tensor buffers, manage complex image resizing, normalize pixel values, and parse raw, unreadable float arrays into something a human could actually use. It was a world of low-level manipulat...