Inserting data science models
In this section, we will explore how we can incorporate data science models into our flows. Your organization might already have data-cleaning code written in R or Python. Your organization might also be using R, Python, or Einstein Discovery and Prediction Builder to score data. For example, you might have a model that looks at customer data and, using an ML algorithm, scores a customer’s propensity to churn. Within a Tableau Prep flow, you can pass your data to any of these technologies to get back new or transformed data and then continue with your flow in Tableau Prep Builder.
It is beyond the scope of this textbook to create and integrate with R, Python, or Einstein models, as each of these technologies requires an extensive combination of installation and/or configuration. For this reason, we will look at the steps to add the models into a flow in the user interface without creating a connection. This will enable you to understand the process...
