Diploma in AI for IoT and Edge Computing
Course provided by Model Institute of Engineering & Technology
5 modules
Explore the fundamentals of Artificial Intelligence & Machine Learning
5-5.5 Level NCrF
National Credit Framework
480 Hours
Flexible Learning
Beginner Level
No prior experience required
Diploma Programs
18 Credits
Course Overview
The Diploma in AI for IoT and Edge Computing trains learners to design and deploy AI models on edge devices for smart system applications. Students work with NVIDIA Jetson Nano, TensorFlow Lite, and ONNX to process sensor data, enable real-time edge inference, and ensure secure, ethical deployments. With applications in agriculture, smart cities, and tourism, the program concludes with an industry-focused capstone project, preparing graduates for roles such as Edge AI Developer, IoT Solutions Engineer, and AIoT Specialist.
Key Learning Highlights
Build and deploy AI models on edge devices like NVIDIA Jetson Nano
Optimize models using TensorFlow Lite and ONNX for low-power environments
Integrate and process data from IoT sensors for real-time edge inference
Apply secure and ethical AI practices in AIoT deployments
Work on region-focused projects in agriculture, smart cities, and tourism
Gain industry experience through capstone projects with local partners
Tools & Platform Used
Learning Outcome
By the end of this course, students will be able to:
Understand AI algorithms, IoT architectures, and edge computing concepts
Deploy and optimize AI models on resource-constrained edge devices
Integrate sensor data processing with IoT protocols for smart applications
Implement secure communication and ethical AI practices in AIoT systems
Develop region-specific AIoT solutions addressing real-world challenges
Demonstrate skills for roles such as Edge AI Developer, IoT Solutions Engineer, and AIoT Specialist
Master the course with just 5 Modules
This course takes learners from the fundamentals of AI, IoT architectures, and edge computing to advanced model deployment on resource-constrained devices. Beginning with core concepts and sensor integration, students progress to hands-on projects using NVIDIA Jetson Nano, TensorFlow Lite, and ONNX for real-time edge inference. The journey concludes with secure, ethical deployment practices and a capstone project delivering AIoT solutions for agriculture, smart cities, and tourism.
Fundamentals of AI and IoT
Learn AI algorithms, IoT architectures, and edge computing principles
Explore sensor technologies and Industry 4.0 applications
Study regional use cases in agriculture, tourism, and smart cities
AI on Edge Devices
Deploy AI models on devices like NVIDIA Jetson Nano
Optimize models using TensorFlow Lite and ONNX for low-power performance
Configure edge devices for efficient AI inference
Sensor Integration and Data Processing
Collect and preprocess sensor data using Python
Implement real-time edge inference for IoT applications
Integrate IoT protocols like MQTT and CoAP for smart systems
Secure IoT and AI Deployment
Apply IoT security and data encryption methods
Implement secure communication protocols for AIoT systems
Integrate ethical AI practices into deployment strategies
Capstone Project
Design and deploy a complete AI-powered IoT solution
Address real-world challenges in agriculture or tourism
Present and validate solutions with industry and academic mentors
Roles
- Edge AI Developer
- Smart Systems Integrator
- IoT Solutions Engineer
- AIoT Specialist
- Embedded AI Engineer
