I haven't come across a real world application of autoencoders before. Usually, for dimensionality reduction I've used PCA or random projections instead.
Most examples I've come across of using autoencoders for dimensionality reduction are usually toy problems. For example, training an autoencoder on MNIST to use logistic regression as the final classifier. I wouldn't call this a practical application since usually there are more relevant vision models that you could apply instead of logistic regression (especially for datasets more complex than MNIST).
What are some non-toy examples of applications of autoencoders (over other dimensionality reduction techniques)? I'm particularly interested in applications on tabular datasets or datasets with sparse features. References to papers, blog posts or anecdotes would all be helpful.