Most machine learning projects don’t fail because of clever modeling mistakes. They fail much earlier, when the data first shows up. The wrong sources, missing edge cases, rushed labels, or quiet quality issues lock in problems that tuning can’t undo later.  Data acquisition in machine learning is where speed, accuracy, bias, and cost all collide.  […]