Overview

Master the integration of Industrial IoT with leading cloud platforms and unlock the power of advanced analytics, digital twins, and machine learning for industrial applications. Discover how to leverage AWS, Azure, and multi-cloud environments to drive innovation and predictive insights. This course empowers learners to implement Industrial IoT solutions using AWS and Azure, covering cloud-based data flows, device management, and analytics pipelines. Participants will explore advanced topics such as predictive analytics, digital twin modeling, and deploying machine learning models across cloud and edge environments. By the end of the course, learners will be able to architect scalable, intelligent IIoT systems that harness the full potential of cloud technologies and AI-driven analytics. The course combines in-depth explanations, industry use cases, and targeted assessments to help learners confidently apply cloud and analytics concepts in industrial contexts. Emphasis is placed on practical integration strategies and the latest advancements in IIoT analytics. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

Syllabus

Building an AWS Industrial IOT Solution

This module introduces the foundational components of building an industrial IoT solution using AWS, including device management, data processing, and secure connectivity. Learners will gain hands-on experience with AWS IoT Core, the AWS SDK, and Greengrass, while exploring best practices for integrating edge devices and managing industrial data streams.

Implementing an Industrial IOT Data Flow with AWS

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.

Performing a Practical Industrial IoT Solution with Azure

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.

Implementing an Industrial IoT Data Flow with Azure

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.

Performing Diagnostic, Maintenance, and Predictive Analytics

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.

Implementing a Digital Twin – Advanced Analytics

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.

Deploying an Analytics Model

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.