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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 tha
by darkpuma 8y ago
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 8y agoI 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.