Managing payload data for Watson OpenScale

Logging payload data

When you send a scoring request, Watson OpenScale processes your model transactions and perturbs data to enable model evaluations. Watson OpenScale scores the perturbed data and stores it as records in a payload logging table within the Watson OpenScale data mart. The data that is stored in the table must contain the same feature and prediction columns from your training data. The table can also include a prediction probability column and timestamp and ID columns to store your data as scoring records. You can view your payload logging table by accessing the database that you specified for the data mart or by using the Watson OpenScale Python SDK as shown in the following example:

Sending payload data

When you provide model details to prepare Watson OpenScale for model evaluations, you must send a scoring request to log payload data so that you can configure evaluations. If you are using IBM Watson Machine Learning as your machine learning provider, Watson OpenScale automatically logs payload data when your model is scored. If you are using an external machine learning provider, you must manually log payload data with a JSON file or a payload logging endpoint to configure evaluations and explainability. For more information, see Payload logging.

After you configure evaluations, you can also use a payload logging endpoint to send scoring requests to run on-demand evaluations. For more information see, Sending model transactions. For production models, you can also upload payload data with a CSV file to send scoring requests.

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Payload logging in Watson OpenScale

Parent topic: Managing data for model evaluations in Watson OpenScale