Hands-on workshop for using automated machine learning in Power BI
Power BI has introduced several machine learning (ML) and artificial intelligence (AI) capabilities into both the Power BI Desktop and Service. In this hands-on workshop, we will use a predictive maintenance scenario to load and explore data – and understand key influencers that drive equipment failure. We will then develop/train/deploy a machine learning model to proactively predict the likelihood of future failure events. Finally, we will explore how Power BI can leverage external ML models and Cognitive Services (e.g., image classification, key phrase extraction).
- Load, model, and visualize data using the Power BI Desktop
- Augment visualizations with R and Python
- Use the Key Influencers visualization to understand key features (and case segmentation)
- Detect anomalies and add smart narratives to your reports
- Use DataFlows to load data in a Power BI workspace
- Use DataFlows to build and evaluate a classification/regression ML Model
- Understand the Prediction Report
- Understand the Prediction Explanation values
- Use DataFlows to make predictions using a trained model
- Leverage a model built in Power BI
- Leverage a model external to Power BI
- Augment data with external ML Models & Cognitive Services
- This workshop is designed be instructor-led (over a ~1 day timespan); see the included PowerPoint for background material and a suggested flow.