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
Micro Program
02 Credits
Course Overview
This course introduces the integration of Artificial Intelligence (AI) with Digital Transformation and Industrial IoT systems. Learners will explore how machine learning and deep learning models enhance IoT applications, and gain hands-on experience in designing AI algorithms optimized for Edge Computing. The program emphasizes workload distribution between edge and cloud environments, culminating in the development and deployment of AI models on real-world edge devices for smart industrial applications.
Key Learning Highlights
Gain a strong foundation in AI applications for Digital Transformation and Industrial IoT.
Learn to apply machine learning and deep learning models to IoT-driven use cases.
Design AI algorithms optimized for Edge Computing environments.
Understand workload placement between edge and cloud platforms for efficiency.
Develop, test, and deploy AI models on edge devices for real-world industrial scenarios.
Tools & Platform Used
Learning Outcome
By the end of this course, learners will be able to:
Explain the role of AI in Digital Transformation and Industrial IoT.
Apply machine learning and deep learning models in IoT applications.
Design and implement AI algorithms tailored for Edge Computing.
Differentiate between edge and cloud computing for AI workload placement.
Build, test, and deploy AI models on edge devices for industrial use cases.
Master the course with just 4 Modules
The course begins with an introduction to AI in Industrial IoT systems, highlighting its role in predictive analytics, decision support, and optimization through real-world use cases. Learners then explore Machine Learning at the Edge, covering architectures, edge AI hardware, and frameworks for efficient on-device intelligence. The next module focuses on building and deploying AI models for edge devices, including data preprocessing, model training, optimization, and real-world sensor integration. Finally, the course presents case studies and emerging trends, showcasing applications in agriculture, surveillance, and automation, while addressing security, privacy, and the future of edge AI technologies.
Introduction to AI for IoT Systems
- Explore the fundamentals of Industrial IoT and the role of AI in predictive analytics and optimization.
- Learn core AI algorithms such as classification, regression, and clustering.
- Analyze real-world use cases in smart factories, predictive maintenance, and process automation.
Machine Learning at the Edge
- Understand edge vs. cloud architectures and their impact on AI deployment.
- Get hands-on with edge AI hardware platforms like NVIDIA Jetson, Coral, and Raspberry Pi.
- Work with software frameworks such as TensorFlow Lite, ONNX, and Edge Impulse for edge intelligence.
Implementing AI Models for Edge Devices
- Perform data collection, preprocessing, and model training using Python (scikit-learn, TensorFlow/Keras).
- Optimize models through techniques like quantization and pruning for edge efficiency.
- Deploy AI models on edge devices with real-time camera and sensor inputs.
Case Studies and Emerging Trends
Study applications of edge AI in agriculture, surveillance, and industrial automation.
Examine security and privacy concerns in edge deployments.
Explore future trends such as federated learning, low-power AI chips, and real-time inference.
Roles
Edge AI Engineer
IoT Data Scientist
Machine Learning Engineer (Edge/Embedded)
AIoT Solutions Architect
Industrial Automation Engineer
Embedded Systems Engineer (AI focus)
Predictive Maintenance Analyst
