In this hands-on tutorial you will deploy a local Python app to test your model.
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This tutorial consists of 6 parts, you can start with part I or any other part, however, the necessary environment is set up in part I.
Part I – data visualization, preparation, and transformation
Part II – build and evaluate machine learning models by using AutoAI
Part III – graphically build and evaluate machine learning models by using SPSS Modeler flow
Part IV – set up and run Jupyter Notebooks to develop a machine learning model
Part V – deploy a local Python app to test your model
Part VI – monitor your model with OpenScale
Let’s imagine we have developed a machine learning model – using AutoAI, SPSS Modeler or a Jupyter Notebook – and now we want to deploy it and make it available to users in a certain form. For the example of customer churn, this could be a UI in which marketing employees can enter data and start a prediction. The data is then sent directly via REST API to a corresponding endpoint and the prediction comes back as a result, which we can then display in the UI.
Just go to this GitHub Repository, follow the instructions to set up the local Python app and hit the prediction button.