Introduction to GenAI in IoT: Powering the Smart Everything Revolution

As IoT devices become central to industries like manufacturing, smart buildings, and operations, understanding how AI enhances connected systems is increasingly valuable. This course introduces you to the convergence of IoT and GenAI—from foundational concepts to real-world applications.

Begin by exploring IoT principles and strategic advantages across industries, then move into AI fundamentals such as machine learning, deep learning, and technologies like voice assistants and recommendation engines. Instructor Rahul Kaundal covers IoT deployment strategies, comparing standalone, in-band, and guard band approaches, and their effects on performance and scalability. Discover how generative AI integrates with IoT, including how large language models (LLMs) function, how to address AI hallucinations, and how to build specialized IoT assistants. By the end of this course, you’ll be prepared to apply these skills to predictive maintenance, AIOps, and natural language simulation.

Learning objectives
Explain the fundamental principles, strategic advantages, and industry-transforming applications of the Internet of Things (IoT).
Differentiate between core AI concepts—including machine learning, deep learning, and generative AI—and analyze how they enable intelligence in systems like voice assistants, recommendation engines, and facial recognition.
Compare various IoT deployment strategies, including standalone, in-band, and guard band approaches, and evaluate their respective trade-offs in terms of spectrum utilization, cost, and performance.
Analyze the role of generative AI and large language models (LLMs) within the IoT ecosystem, and assess techniques to mitigate challenges like hallucinations.
Design and justify the application of generative AI for specialized industry use cases, including predictive maintenance with anomaly explanation, autonomous IoT operations (AIOps), and natural language-based querying and simulation of IoT systems.