This module guides learners through the practical implementation of an industrial IoT data flow using AWS services. You will explore topics such as data storage, stream and batch processing, event management with IoT Events, and visualization with Grafana. By the end, you'll understand how to build and monitor a complete IIoT pipeline from edge devices to cloud dashboards.

This module guides learners through implementing an industrial IoT solution using Azure technologies. You will explore how to connect edge devices with Node-RED, leverage Azure IoT Edge for enhanced edge computing, and monitor device status using Device Twins. Practical exercises will reinforce your understanding of integrating and managing IIoT systems on the Azure platform.

This module guides learners through configuring and managing industrial IoT data flows using Azure services. You will explore Azure Stream Analytics, create user-defined functions for data transformation, and integrate data with Azure Synapse and Cosmos DB for advanced analytics and visualization.

This module explores the key types of analytics used in Industrial IoT, including diagnostic, maintenance, and predictive approaches. Learners will examine both data-driven and physics-based modeling, understand the role of infrastructure in analytics deployment, and gain hands-on experience with techniques like ARIMA and one-class SVM for anomaly detection. Practical exercises include exploratory data analysis and model building for production optimization.

This module explores advanced analytics techniques for implementing digital twins, including unsupervised learning, deep learning, and physics-based modeling. Learners will gain hands-on experience developing and testing neural network models using Python, PyTorch, and pandas. Practical exercises guide you through preparing data, building models, and interpreting results in an IIoT context.

This module guides learners through the practical steps of deploying analytics models using leading cloud platforms such as Azure, AWS, and GCP. You will explore setting up cloud workspaces, managing data storage, running experiments, and automating deployment workflows. By the end, you'll understand how to operationalize machine learning models in real-world industrial environments.