The most important decision in an ML system is usually not the model or the tuning. It is how you define the problem.

A well-trained model solving the wrong problem will confidently optimize for the wrong outcome. A simpler model, aligned with the real goal, will deliver more value.

Good framing decides what data you collect, which labels you trust, what the model optimizes, and how you know whether the system improved.