I've built a short pipeline for using deep learning over images (e.g. for image categorization) and indexing them into Elasticsearch. I thought this could be of interest to other practitioners as well. In a nutshell, this allows to tag and retrieve image documents via ES even when no caption is available.
On the technical side, a deep learning server directly pushes the image classification results into an instance of ES, so that no glue code is necessary.
See http://www.deepdetect.com/tutorials/es-image-classifier/ for a short tutorial, and https://github.com/beniz/deepdetect for the more generic deep learning server (that relies on the Caffe library).
Many of the machine learning pipelines that I am involved with include an ES instance so my guess is that it may probably be the case for others as well.
This type of coupling is pretty generic and should capture a larger set of common cases, from text classification to prediction based on a variety of features and data. A typical extension would be an image similarity search ability, much more powerful than the existing LIRE plugin.
Let me know your thoughts and issues if any,