When NOT to Use Deep Learning

• Tiny dataset with easily engineered features? Try simpler ML (like linear or tree-based models).
• Need perfect interpretability or strict guarantees? DL may be harder to justify.
• Low compute budget or latency constraints? A smaller model may be better.

Rule of thumb: start simple, scale up when the problem/data demands it.