AI for IoT using Edge Devices
Course provided by Model Institute of Engineering & Technology
5 modules
Explore the fundamentals of Artificial Intelligence & Machine Learning
5 Level NCrF
National Credit Framework
60 Hours
Flexible Learning
Beginner Level
No prior experience required
Micro Credit Course
02 Credit
Course Overview
This course introduces learners to the fundamentals of deploying Artificial Intelligence (AI) on Internet of Things (IoT) edge devices. It provides hands-on exposure to microcontrollers, edge AI frameworks, and real-time inference on devices like Raspberry Pi and NVIDIA Jetson Nano. Participants will learn how to collect sensor data, train lightweight machine learning models, and deploy them on edge hardware for real-time decision-making without relying on cloud connectivity. The course emphasizes practical implementation and showcases applications in smart systems, predictive maintenance, and intelligent automation.
Key Learning Highlights
Understand the fundamentals of IoT architecture and edge computing.
Explore AI algorithms optimized for edge devices.
Learn to collect, process, and analyze IoT data locally.
Implement real-time machine learning models on edge devices.
Hands-on projects integrating sensors, devices, and AI models.
Optimize AI models for low-latency, energy-efficient edge deployment.
Tools & Platforms Used
Python
Jupyter Notebook
Raspberry Pi
Arduino
Jetson Nano
TensorFlow Lite
PyTorch
AWS IoT
Learning Outcome
By the end of this course, students will be able to:
Design and implement IoT systems integrated with AI models.
Deploy machine learning models efficiently on edge devices.
Process and analyze IoT data in real-time at the edge.
Optimize models for performance, power, and memory constraints.
Develop end-to-end IoT-AI projects with hands-on practical experience.
Master the course with just 5 Modules
This course takes learners from the fundamentals of Generative AI and prompt construction to advanced tuning techniques. Beginning with the principles behind AI-generated text and images, participants progress to hands-on experimentation with real-world use cases. The journey ends with a deep dive into emerging trends, ethical implications, and future opportunities in the rapidly evolving field of GenAI.
AI and IoT Integration Basics
- Introduction to Artificial Intelligence and IoT ecosystems
- Edge vs. cloud computing: architecture, latency, and power trade-offs
- Use cases: smart homes, predictive maintenance, surveillance, health monitoring
Working with NVIDIA Jetson Nano
- Jetson Nano hardware and setup
- JetPack SDK installation and introduction
- Running pre-trained deep learning models on Jetson Nano
- Working with Linux shell, terminal commands, and Python on Jetson
Sensor and Peripheral Interfacing
- Basics of GPIO pins and interfacing
- Camera and sensor integration (temperature, ultrasonic, PIR, etc.)
- Data acquisition using Python
- Real-time data streaming, buffering, and logging
Model Optimization for Edge Inference
- Overview of TensorRT and ONNX
- Converting and deploying AI models (YOLOv5, MobileNet, etc.)
- Quantization, pruning, and runtime optimizations
- Measuring performance: inference time, memory footprint
Capstone Project – Smart AI IoT System
Each student/team will design and deploy an edge-AI enabled IoT solution, such as:
- Smart surveillance system using real-time object detection
- Industrial fault detection using camera and sensor fusion
- Health monitoring device with alerts and dashboard
- Waste classification bin using real-time inference
Roles
- Edge AI Developer
- Smart Systems Integrator
