Hi @LDogg,
You can do predictive maintenance something like this:
Start by streaming sensor or IoT data like temperature, pressure, vibration, etc. into Delta Lake using tools like Structured Streaming or Delta Live Tables.
Next, we can process and engineer features, for example, rolling averages, trend detection, or sudden spikes using Spark and Pandas UDFs.
Then, we can use AutoML or build custom machine learning models like classification, anomaly detection, or time-series forecasting to predict potential failures.
Once the model is ready, we can run it in real-time on incoming data or as a scheduled batch job to flag any risks early.
With MLflow, we can track the experiments, manage model versions, monitor performance, and even automate retraining if needed.
