In this module, the students will learn how to design a solution for ingesting and preparing manufacturing device sensor data, as well as detecting anomalies in sensor data and creating, training, and deploying a machine learning model which can predict when device maintenance will become necessary.

In this module, the students will learn how to build a cloud processing and machine learning solution for IoT data. The students will begin by deploying a factory load simulator using Azure IoT Edge to write into Azure IoT Hub, following the recommendations in the Azure IoT reference architecture. The data in this simulator represents sensor data collected from a stamping press machine, which cuts, shapes, and imprints sheet metal. The rest of the module will show how to implement an event sourcing architecture using Azure technologies ranging from Cosmos DB to Stream Analytics to Azure Functions to Azure Database for PostgreSQL.
Using factory-generated data,the students will learn how to use the Anomaly Detection service built into Stream Analytics to observe and report on abnormal machine temperature readings, also student will learn how to apply historical machine temperature and stamping pressure values in the creation of a machine learning model to identify potential issues which might require machine adjustment, also will deploy this predictive maintenance model and generate predictions on simulated stamp press data.