The GCP (Google Cloud Platform) ML Node allows a workflow to send data to Google ML and store the resulting predictions on the workflow payload.
If you’re new to Google ML, TensorFlow, or machine learning in general, you may want to take some time to look through the following resources:
Configuration for the node is broken up into four sections.
A service account auth token is required for the workflow to authenticate with Google ML. You may enter this token one of two ways:
- JSON Template accepts a JSON template for the token.
- Payload Path accepts a payload path for the token.
Specify the name of the model for which you want to get predictions. You may optionally specify the model version as well; if no version is provided the model’s default version will be used. Before you can get predictions from a model, you’ll first need to train it with sample data and then deploy it.
The instances path is a payload path that points to the data you want to get predictions for.
The output path is a payload path at which to place the prediction results.