Summary
In this chapter, you learned how Power BI interacts with Microsoft AI services by default through the Power BI Desktop and dataflow features. You also learned that by using AutoML platforms, you can get around the licensing issue (PPU license or Premium capacity) that Power BI presents for interfacing with Microsoft AI services. You used both an on-premises AutoML solution (PyCaret) and Azure AutoML on the cloud to solve a binary classification problem. You also used Azure AI Language to perform some sentiment analysis directly using a Python SDK.
You’ve learned that AI enrichment is mostly done in Power Query (which provides access to the web using the data gateway), although you’ve seen a case where it can be convenient to use a ML model directly in a Python visual.
In the next chapter, you will see how to implement data exploration of your dataset in Power BI.
