Classical machine learning, and very often the model that should have been tried before anything larger.
Tabular data, classification and regression on structured records — where a gradient-boosted tree quietly beats a neural network. Most business problems arrive as rows and columns, and most of them are solved here for a fraction of what the alternative costs to train and serve.
Unstructured text, images and audio. That is where deep learning earns its cost. It is, however, the fastest way to find out whether you needed deep learning at all.
A baseline built first, so any more complicated model has something honest to beat. Building it first also surfaces the data problems while they are still cheap to fix, and often the baseline turns out to be good enough to ship — a better outcome than a model nobody can maintain.
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