That's setting the goalposts a bit narrow in terms of practical applications of ML. You can apply deep learning to solve problems aren't public facing, such as analytics forecasting.
Additionally, a lot of public ML products don't use, and don't need to use, deep learning (e.g. NLP applications).
Analytics forecasting doesn't really seem like a good candidate for deep learning. There are plenty of established methods for forecasting that are simpler, more robust and generally more effective in terms of effort to reward.
Additionally, a lot of public ML products don't use, and don't need to use, deep learning (e.g. NLP applications).