AI for IoT using Edge Devices

Course provided by Model Institute of Engineering & Technology

4 modules

Explore the fundamentals of Cyber Physical Systems

5 Level NCrF

National Credit Framework

60 Hours

Flexible Learning

Beginner Level

No prior experience required

Certificate Program

02 Credits

Course Overview

This course introduces learners to the integration of Artificial Intelligence (AI) with Industrial Internet of Things (IIoT) systems, emphasizing its role in driving Digital Transformation. Learners will gain hands-on experience in applying machine learning and deep learning models in IoT contexts, with a focus on designing and implementing AI algorithms optimized for Edge Computing environments. The course highlights the differences between edge and cloud workload placement, equipping learners to make informed architectural choices. By the end, learners will be able to develop, optimize, and deploy AI models on edge devices for real-world industrial applications such as predictive maintenance, smart factories, and automation.

Key Learning Highlights

Gain a strong foundation in the role of AI for Digital Transformation and IIoT systems.
Learn to apply machine learning and deep learning techniques in IoT applications.
Design and optimize AI algorithms for Edge Computing environments.
Understand edge vs. cloud workload placement for efficient AI deployment.
Develop, test, and deploy AI models on real-world edge devices.

Tools & Platform Used

Learning Outcome

By the end of this course, learners will be able to:

Explain how AI drives digital transformation in Industrial IoT systems.
Apply machine learning and deep learning models to IoT datasets.
Design AI algorithms that can run efficiently on edge devices.
Compare and choose between edge and cloud platforms for AI deployment.
Build, test, and deploy AI models on practical edge hardware.

Master the course with just 4 Modules

This course begins with an introduction to AI for Industrial IoT systems, covering the role of AI in predictive analytics, decision support, and real-world industrial applications. Learners then explore machine learning at the edge, including hardware platforms like NVIDIA Jetson, Coral, and Raspberry Pi, along with software frameworks such as TensorFlow Lite and Edge Impulse. The course progresses to implementing AI models on edge devices, focusing on data preprocessing, model training, optimization, and deployment on sensor-enabled hardware. Finally, learners examine real-world case studies and emerging trends, including smart agriculture, surveillance, automation, federated learning, and low-power AI solutions, to understand practical applications and future directions.

Introduction to AI for IoT Systems

Overview of Industrial IoT and the role of AI in predictive analytics and decision support
Introduction to AI algorithms: classification, regression, and clustering
Real-world use cases in smart factories, predictive maintenance, and process optimization

Machine Learning at the Edge

Edge vs. Cloud architectures and their impact on AI deployment
Edge AI hardware platforms: NVIDIA Jetson, Coral, Raspberry Pi
Edge AI software frameworks: TensorFlow Lite, ONNX, Edge Impulse, with evaluation metrics (latency, power, accuracy)

Implementing AI Models for Edge Devices

Data collection, preprocessing, and model training using Python (scikit-learn, TensorFlow/Keras)
Model optimization techniques for edge deployment: quantization, pruning, and compression
Deployment of AI models on real devices with sensor and camera inputs

Case Studies and Emerging Trends

Edge AI applications in smart agriculture, surveillance, and industrial automation
Security, privacy, and performance considerations for edge AI systems
Emerging trends: federated learning, real-time inference, and low-power AI chips

Roles

Edge AI Developer
IoT Data Scientist
Embedded AI Engineer
Industrial IoT Solutions Engineer
AI Model Deployment Specialist
Smart Manufacturing Engineer