Wind Turbine Predictive Maintenance on Azure Databricks – Part 5: Training Models and Tracking with MLflow
In Part 5 of 6 of this sequence to implement a real-time Wind Turbine Predictive Maintenance application, we train and track two predictive models. The first determines the remaining useful life of each turbine based on its history. The second determines the power 6 hours ahead of time based on current conditions. These predictions will help us optimize the power and cost of our operations.
