Course Overview
Embedded AI represents the next frontier of Artificial Intelligence, bringing machine learning intelligence directly to edge devices, sensors, and embedded hardware.
In this course, you will learn how to optimize AI models for constrained devices, deploy machine learning on microcontrollers and edge hardware, and build real-time, on-device applications.
Through practical labs and real-world projects, participants will build embedded AI solutions using frameworks such as TensorFlow Lite, Edge Impulse, and ONNX Runtime.
By the end of this course, learners will be able to develop and deploy production-ready embedded AI applications for computer vision, sensor fusion, and predictive maintenance use cases.
Course Distinction
What makes our course unique?
Unlock the Future of Edge Intelligence
Master the concepts behind deploying AI directly on devices, transforming how products sense, decide, and act in real time.
Expert-Led Industry Training
Learn from embedded systems and AI practitioners with real-world experience building intelligent edge devices.
Hands-On Learning
Build real embedded AI models and deploy them to physical edge hardware through guided labs and capstone projects.
Practical Enterprise Use Cases
Learn how Embedded AI is transforming industries like manufacturing, automotive, healthcare, consumer electronics, and industrial IoT.
Course Content
- Introduction to Embedded AI and edge computing
- Edge AI vs cloud AI: trade-offs and use cases
- Embedded systems fundamentals
- Overview of the embedded AI ecosystem
- Machine learning fundamentals for constrained devices
- Selecting models for embedded deployment
- Data collection and preprocessing for edge AI
- Evaluating model performance on limited hardware
- Model quantization techniques
- Pruning and compression strategies
- Introduction to TinyML
- Balancing accuracy, size, and latency
- Working with TensorFlow Lite
- Building with Edge Impulse
- Using ONNX Runtime for edge inference
- Comparing embedded AI toolchains
- Microcontrollers and AI accelerators
- ARM Cortex-based development boards
- NVIDIA Jetson and Raspberry Pi platforms
- Selecting hardware for embedded AI projects
- Computer vision at the edge
- Sensor fusion techniques
- Predictive maintenance applications
- Real-time inference and latency optimization
- Firmware integration for AI models
- Power optimization for battery-powered devices
- Production deployment best practices
- Monitoring and updating deployed models
Capstone Project
Build and deploy a real-time embedded AI application on edge hardware, demonstrating on-device inference for a practical industry use case.
Key Features
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Instructor-led interactive sessions -
Real-world embedded AI case studies -
Hands-on model optimization and deployment -
Industry recognized certification -
Access to embedded AI development resources -
Capstone project
Skills Covered
- TinyML
- Edge AI Deployment
- Model Quantization & Optimization
- Embedded Systems Programming
- Computer Vision at the Edge
- IoT & AI Integration
- TensorFlow Lite & ONNX Runtime
- Real-Time AI Applications
Advancements
Business Impact
FAQ?
Customized Corporate Training
We also provide corporate training programs tailored for organizations looking to build intelligent edge devices and embedded AI products.
✔ Custom curriculum
✔ Industry-specific use cases
✔ Flexible training delivery
✔ Enterprise consulting support
Who Should Attend
Embedded Systems Engineers
IoT Developers
AI & ML Engineers
Hardware Engineers
Robotics Engineers
Robotics Engineers
Build the Future with Embedded AI
Become a specialist in Edge Intelligence and On-Device AI Systems.
✔ Learn from embedded AI industry experts
✔ Work on real edge AI projects
✔ Earn certification
Testimonial
What people are say?

















