Foundations of Cloud, IoT, and Edge Machine Learning

Connecting smart devices to the cloud and running intelligent algorithms directly on hardware is reshaping modern industry. This text-based course guides you from the foundational concepts of the Internet of Things (IoT) to deploying machine learning models at the network edge. You will transition from understanding basic sensor networks to orchestrating complete IoT-ML pipelines. By studying real-world architectural patterns, reading structured code examples, and working through practical scenarios, you will learn how to balance processing power between the cloud and edge devices while maintaining system security and efficiency. What you'll learn: - Understand the core terminology of IoT, cloud computing, and edge intelligence - Configure secure data communication protocols like MQTT and HTTP for device-to-cloud messaging - Prepare and preprocess sensor data using modern data libraries - Optimize machine learning models for resource-constrained edge hardware using quantization techniques - Deploy containerized ML models to edge nodes and monitor their performance - Design hybrid architectures that balance local processing with cloud analytics The course begins with essential definitions of cloud and edge topologies before moving into data ingestion, model optimization, and deployment workflows. You will explore practical text-based walkthroughs and architectural blueprints that prepare you for real-world projects. This course is designed for beginners, developers, and aspiring systems architects who want to understand the intersection of IoT and AI. No prior experience with cloud platforms or machine learning is required. Start building your understanding of smart, connected edge systems today.