Programme Parameters
Advanced Postgrad
4 Weeks (40 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Edge AI Course | Learn AI Deployment on Edge Devices is a mentor-based online program designed to help learners understand how artificial intelligence can be deployed directly on edge devices instead of relying only on cloud-based systems. The course introduces participants to the concepts, tools, and workflows required to build compact, efficient, and real-time AI applications for embedded systems, IoT devices, mobile devices, smart sensors, and low-power hardware platforms. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Edge AI Course | Learn AI Deployment on Edge Devices
is a mentor-based online program designed to help learners understand how artificial intelligence can be deployed directly on edge devices instead of relying only on cloud-based systems. The course introduces participants to the concepts, tools, and workflows required to build compact, efficient, and real-time AI applications for embedded systems, IoT devices, mobile devices, smart sensors, and low-power hardware platforms.
📋 Course Objectives
1. Apply Artificial Intelligence methods to authentic research and industry problems.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in Artificial Intelligence
• R&D engineers and working professionals applying Artificial Intelligence in industry
• Academics and educators building research or teaching capacity in Artificial Intelligence
🚀 Key Learning Outcomes
• A demonstrable Artificial Intelligence project for your research or industry portfolio.
• 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
When the Model Has to Leave the Cloud
• Latency, privacy, bandwidth and offline operation as the real drivers of on-device inference
• The round-trip cost of a cloud call, and the workloads where it is unacceptable
• Where edge is the wrong choice — large models, infrequent inference, abundant connectivity
Making a Model Small Enough to Fit
• Post-training quantization and quantization-aware training (INT8) and the accuracy they cost
• Pruning and knowledge distillation, and how far each shrinks a network before it breaks
• Reading a size/latency/accuracy trade-off curve instead of chasing one number
Matching the Model to the Silicon
• MCUs, single-board computers and accelerators — ESP32, Raspberry Pi, Jetson, Coral Edge TPU
• RAM, flash and the operator support that decide whether a model will even load
• TensorFlow Lite, TFLite Micro and ONNX Runtime as the deployment runtimes
Getting Inference Onto the Device
• Exporting and converting a trained model, then benchmarking real latency and power draw
• Thermal throttling and duty cycles — the reason bench numbers do not hold in the field
• Profiling memory and startup time to find the bottleneck that is not the model
A Working On-Device Application
• Deploying a vision or keyword-spotting model to a physical device end to end
• Measuring accuracy on-device against the training figures, and explaining the gap
• Over-the-air update and drift monitoring so the deployed model can be maintained
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Edge AI |
| Covered Tool / Platform | Artificial Intelligence |
| Covered Tool / Platform | TensorFlow Lite |
| Covered Tool / Platform | ONNX Runtime |
| Covered Tool / Platform | PyTorch Mobile |
| Covered Tool / Platform | TinyML |
| Covered Tool / Platform | Model Optimization |
| Covered Tool / Platform | Quantization |
| Covered Tool / Platform | Pruning |
| Covered Tool / Platform | IoT Devices |
| Covered Tool / Platform | Embedded Systems |
| Covered Tool / Platform | Real-Time Inference |
Frequently Asked Questions
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