Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Edge AI: Deploying AI on Edge Devices teaches how to run intelligence where the data is, not in the cloud. You learn the constraints of edge and embedded hardware, how to compress and optimise models with quantisation and pruning, and how to deploy them with edge frameworks for real-time, private, low-power inference. The course covers applications from IoT and wearables to cameras and robotics. You finish able to deploy an ML model to an edge device. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers edge AI — deploying machine-learning models on edge and embedded devices for fast, private, on-device inference.
📋 Course Objectives
1. Explain edge and embedded hardware constraints.
2. Compress models with quantisation and pruning.
3. Deploy with edge and TinyML frameworks.
4. Achieve real-time on-device inference.
5. Apply edge AI to IoT and devices.
👥 Who Should Enroll?
• Embedded and IoT engineers
• ML engineers deploying to devices
• Hardware and product teams
• Students of edge AI
🚀 Key Learning Outcomes
• The ability to deploy AI on edge devices.
• An on-device inference perspective.
• An edge-AI project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Module 1 — Foundations of Edge AI
Introduction to Edge AI and embedded intelligence • Difference between cloud AI, fog AI, and edge AI • Advantages, limitations, and real-world relevance of edge deployment • Core components of an edge AI ecosystem: sensors, processors, connectivity, and inference engines
Module 2 — Edge Devices and Hardware Platforms
Overview of edge hardware architectures • Microcontrollers, embedded systems, SoCs, GPUs, TPUs, and NPUs • Comparative study of popular platforms: Raspberry Pi, NVIDIA Jetson, Arduino, Coral, ESP32 • Hardware selection criteria for AI deployment
Module 3 — AI/ML Fundamentals for Edge Deployment
Refresher on machine learning and deep learning concepts • Model types commonly used in edge AI: CNNs, RNNs, Transformers, TinyML models • Training vs inference: understanding deployment constraints • Performance metrics for edge intelligence: latency, accuracy, memory, and power consumption
Module 4 — Data Acquisition and Preprocessing for Edge AI
Sensor data collection and real-time input streams • Data preprocessing pipelines for image, audio, video, and time-series data • Feature engineering for resource-constrained environments • Handling noisy, incomplete, and streaming data at the edge
Module 5 — Model Optimization for Edge Devices
Model compression techniques: pruning, quantization, and knowledge distillation • Lightweight neural network architectures for edge deployment • Trade-offs between model size, speed, and accuracy • Optimization tools and frameworks for efficient inference
Module 6 — Edge AI Deployment Frameworks and Toolchains
Introduction to TensorFlow Lite, TensorRT, ONNX Runtime, OpenVINO, and Edge Impulse • Model conversion and compatibility across platforms • Building end-to-end deployment pipelines • Debugging, benchmarking, and monitoring deployed models
Module 7 — TinyML and Real-Time Edge Intelligence
Fundamentals of TinyML for microcontroller-based AI • Real-time inference on low-power embedded systems • Event-driven AI applications on constrained devices • TinyML use cases in healthcare, agriculture, manufacturing, and smart systems
Module 8 — Security, Privacy, and Reliability in Edge AI
Security challenges in edge AI systems • Privacy-preserving AI and on-device intelligence • Robustness, fault tolerance, and adversarial considerations • Ethical and regulatory concerns in edge deployment
Module 9 — Applied Edge AI Projects and Case Studies
Computer vision applications on edge devices • Predictive maintenance and industrial monitoring • Smart healthcare and wearable AI systems • Reproducible hands-on deployment workflow using Python and embedded platforms
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | AI Algorithms |
| Covered Tool / Platform | Artificial Intelligence |
| Covered Tool / Platform | Data Privacy |
| Covered Tool / Platform | Device Interoperability |
| Covered Tool / Platform | Distributed Computing |
| Covered Tool / Platform | TensorFlow Lite |
| Covered Tool / Platform | PyTorch Mobile |
| Covered Tool / Platform | ONNX Runtime |
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