- Challenge 4 - Building Machine Learning in Power BI should be done successfully.
Now that we have created an AML Designer based model and deployed that to an endpoint, we understand the process of model creation. We can now use that to craft an AutoML run, deploy the best model to and endpoint, and finally use that endpoint back in Power BI.
- A published BikeBuyer model endpoint hosted in Azure ML using AutoML.
- Demonstrate usage of the API.
- Create a new dataflow in Power BI to leverage the published API.
- None
Description | Links |
Create, explore, and deploy automated machine learning experiments with Azure Machine Learning studio | https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-portal-experiments |
Azure Machine Learning integration in Power BI | https://docs.microsoft.com/en-us/power-bi/service-machine-learning-integration |
Optional challenge (Building Machine Learning Models in Azure Machine Learning Designer) >