When to Use (and Not Use) Deep Learning

Deep learning has achieved remarkable things—image recognition, language understanding, game playing. But it's not the right tool for every job. Here's how to decide.

Deep learning shines with unstructured data and complex patterns. Images, audio, text, video—problems where the relevant features aren't obvious and need to be learned. If you're classifying photos or understanding documents, deep learning is probably the right choice.

For tabular business data, simpler models often work as well or better. Gradient boosted trees (XGBoost, LightGBM) are competitive with or superior to deep learning for most structured data problems. They're faster to train, easier to interpret, and require less tuning.

Deep learning requires more data. Millions of examples for complex problems. If you have thousands, you're probably better off with traditional ML. Transfer learning can help but isn't always applicable.

Interpretability is a real concern. Deep learning models are black boxes. If you need to explain decisions to regulators, customers, or stakeholders, simpler models may be necessary even if slightly less accurate.

Compute costs matter. Training deep learning models is expensive. So is inference at scale. A model that's 1% more accurate but costs 10x to run might not be worth it.

The hype cycle makes everyone want to use deep learning. Resist. Use the simplest approach that solves your problem. Deep learning is powerful but not always necessary.