I have built computer vision products at my startup. I was familiar with some machine learning before, but only really learned to apply it on the job. Fast forward 7 years and the initial product and others that followed have been very successful. Machine learning is only a part of the whole product though.
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.
Google Photos. It has great search over collections of pictures. You can search for pictures of dogs, sunsets, trees, even pictures of store receipts (which is super convenient for filing expense reports).
