Google Cloud Platform
Prediction API

Trainedmodels: update

Requires authorization

Add new data to a trained model. Try it now.

Add new data to a trained model.

Adding new data to a trained model is called streaming training. Streaming training trains a previously trained model against new examples. This is useful if you have a regular stream of new information that you'd like to add to your model as it becomes available, rather than having to recompile, re-upload, and retrain the data with batches of new data. The model is retrained after it receives N new examples (where N is a small number), or after a few minutes if it receives more update requests.

Note that the system may weight newer streamed examples more than earlier examples. If you do not want this, you should add the examples to your training data and retrain the system against all the data by calling prediction.trainedmodels.insert.

Note: If you retrain a model against its original training data file, all the streamed data will be lost. If you want to retain the streamed data, you must store it and update the model data yourself.


HTTP request



Parameter name Value Description
Required parameters
id string The unique name for the predictive model.


This request requires authorization with at least one of the following scopes (read more about authentication and authorization).


Request body

In the request body, supply data with the following structure:

  "label": string,
  "output": string,
  "csvInstance": [
Property name Value Description Notes
label string The class label of this instance
output string The generic output value - could be regression value or class label
csvInstance[] list The example data as an array of columns, in the same format as the CSV file.


If successful, this method returns a Trainedmodels resource in the response body.

Try it!

Use the APIs Explorer below to call this method on live data and see the response. Alternatively, try the standalone Explorer.