Qlik AutoML: How to generate predictions via API realtime-predictions with Python
In a previous article, I outlined the steps to POST an API call to realtime-predictions with Postman. In this article, we will look at how to do this in Python and send a file with records to predict against.
As before, we use iris.csv dataset, a common data science example. Variety is the target variable.
Upload, train and deploy the model for iris.csv. Seethis article for steps on how to deploy a model.
XGBoost Classification was the champion model which I choose to deploy.
The information in this article is provided as-is and to be used at own discretion. Depending on tool(s) used, customization(s), and/or other factors ongoing support on the solution below may not be provided by Qlik Support.