[brianshaler]

I appreciate the detailed response! My use case is yet another indie web, run-your-own-server kind of thing that processes content my friends post online (from tweets to shared articles) and can predict whether or not it is relevant to me based on extracted topics, source, my context/location, and whatnot. Both the source data and training data comes in at a trickle (though I'm pondering ways to propagate training data throughout friend-of-friend networks) and is processed in the background rather than on-demand, so my performance characteristics are very different than most of the use cases I've seen.

I'm trying to keep the system frugal with memory but liberal with persistent storage, since you can run a commodity instance 24/7 and mount a pretty large volume for fairly cheap. It'll be slow, for sure, if there's only one user for each installation it won't need to worry about handling many queries per second.

[barneso]

It seems that MLDB would be a decent fit for this use-case. You would be able to do pre-processing in the background continuously, and predictions could do a significant amount of work on-demand. Depending upon the size of the overall training set, you might need to spin up a larger server for an hour or so to retrain a model... but if you set the system up right, the model would only need to be trained infrequently as most of the work would be done online. The more preprocessing you can do in the background, the richer and smaller the data that would go into the training phase.

MLDB can memory-map some kinds of datasets which would also help with the low memory-to-datasest size ratio.

Please feel free to reach out (jeremy at datacratic) if you'd like to discuss further.