Enterprise AI-Powered Predictive Maintenance Platform
summary
An end-to-end predictive maintenance pipeline that ingests simulated temperature and vibration from sensors via IoT Hub, batches to 10-second windows with Stream Analytics, stores JSON time-series in Azure Cosmos DB, exports CSV via the Python SDK, trains anomaly detectors (isolation forest or LSTM) and writes anomaly flags back into a predictions container for visualization in Power BI. Claims single-digit millisecond read latency, automatic scaling, weekly retraining and alerts; demo shows aggregated averages every 10 seconds and anomaly labels successfully appearing in the dashboard.
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chapters (17)
- 0:00Enterprise AI predictive maintenance intro
- 1:05Reactive to predictive maintenance
- 3:57Traditional maintenance challenges
- 5:04Solution with Azure Cosmos DB and AI
- 6:52Azure Cosmos DB for real-time sensor data
- 7:33IoT data ingestion with Azure IoT Hub
- 8:16IoT infrastructure setup tools
- 9:01Simulating sensor data for IoT Hub
- 9:45Cosmos DB database and container setup
- 10:08Real-time analytics with Stream Analytics
- 11:36Exporting data for model training
- 13:48Streaming data with SQL queries
- 14:53Python SDK for training queries
- 15:16Training anomaly detection models
- 16:02Deploying predictions to Cosmos DB
- 17:08Power BI dashboard visualization
- 17:50Best practices for Cosmos DB and ML
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