I am pretty new to using Google AutoML and I was wondering what the best practice was in the following scenario.
My goal is to update a Google AutoML Translate model without having to change the API call to get translations, and I am not sure if this is possible.
Currently the only way to update a AutoML Translate model is to create a new model, base it on the old one, and train it on the new examples (This is at least what seems to be the case). And when you make an API request to get a translation, you must specify which model you want to use by giving the identifier of that model. Because the old version of the model and the new version have different identifiers does this mean that every API call must be changed so that it uses the new model? Is there any way around changing the API call?
First of all, indeed the only way to update an AutoML Translate model is to create a new one, base it on the old one, and train it with the new examples. This is a clear security measure so you do not loose the old model in the process. Although on paper training with more sentences should help the model accuracy/performance, doing so might hinder the accuracy instead.
Second of all, the API call needs to be changed accordingly. You could code the API call in a way that uses the last model submitted so it does not need to be changed every time you update the model.
To do so, the first idea that comes to my mind is using a cloud function that gets triggered once a model is trained/created and stores the model-id in a bucket in GCS that the code performing the API calls recovers.
Nevertheless, the model performance should be assessed before assigning the translation calls from one model to the other, so I do not recommend simply changing it to the newest version without additional checks unless it is for testing purposes.