[darkpuma]

Time to train a model matters for applications where you want to have end users training models on their own computers without spending so much CPU/GPU time that they have to plan their day around it.

Consider for instance an RSS reader that classifies articles to determine whether or not to interrupt the user with a notification. This should be fast to train and update the model on the fly every time the user enters a correction (e.g. 'this article actually isn't interesting', or 'interrupt me with articles like this in the future'.)

[995533]

I would not retrain such a model on all data, just do online updates. Also I still think for that use case training times and latency are negligible (nobody cares or nobody notices any difference between training a BoW and bi-LSTM.)

If you are deploying on resource-constrainted devices (IE: low-end PC's without GPU), it is not unusual to take a lot of time training a model on a very powerful computer (which nobody cares about), then distilling or transfering the result for test time.