CODEWITHSUNDEEP
ml.net
AHagemann
AHagemann

Reputation: 11

How to train ML .Net model in runtime

is there any way to train an ml .net model in runtime through user input? I've created a text classification model, trained it local, deployed it and now my users are using it.

Needed workflow:

Text will be categorized, category is displayed to user, he can accept it or select another of the predefined categories, than this feedback should train the model again.

Thanks!

Upvotes: 1

Views: 1045

Answers (1)

Zruty
Zruty

Reputation: 8667

What you are describing seems like online learning.

ML.NET doesn't have any true 'online' models (by which I mean, models that can adapt to new data example by example and instantaneously refresh): all ML.NET algorithms are 'batch' trainers, that require a (typically large) corpus of training data to produce a model.

If your situation allows, you could aggregate the users' responses as 'additional training data', and re-train the model periodically using this data (in addition to the older data, possibly down-sampled or otherwise decayed).

As @Jon pointed out, a slight modification of the above mechanism is to 'incrementally train an existing model on a new batch of data'. This is still a batch method, but it can reduce the retraining time.

Of ML.NET's multiclass trainers, only LbfgsMaximumEntropyMulticlassTrainer supports this mode (see documentation).

It might be tempting to take this approach to the limit, and 'retrain' the model on each 'batch' of one example. Unless you really, really know what you are doing, I would advise against it: more likely than not, such a training regime will be overfitting rapidly and disastrously.

Upvotes: 1

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