| Attribute | Detail |
|---|---|
| Format | Self-Paced Online Course |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹8499 / $112 |
| Tools | Edge AI Artificial Intelligence TensorFlow Lite ONNX Runtime PyTorch Mobile TinyML Model Optimization Quantization Pruning IoT Devices Embedded Systems Real-Time Inference |
About the Edge AI Course | Learn AI Deployment on Edge Device 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. The program covers key areas such as edge computing, embedded AI, TinyML, model optimization, quantization, pruning, TensorFlow Lite, ONNX Runtime, PyTorch Mobile, microcontroller-based AI, sensor data processing, real-time inference, and deployment challenges. Through guided learning and dry lab activities, participants gain practical exposure to how AI models are trained, converted, optimized, and deployed on resource-constrained edge devices for real-world applications.
Aim
The aim of this course is to introduce participants to the complete workflow of Edge AI development and deployment. The program focuses on helping learners understand how AI models can run efficiently on local devices with limited memory, processing power, and connectivity while enabling faster, more secure, and energy-efficient intelligent systems.
Learning Objectives
- Understand the fundamentals of Edge AI and its role in modern intelligent systems.
- Learn how AI models are optimized for deployment on edge and embedded devices.
- Explore tools such as TensorFlow Lite, ONNX Runtime, PyTorch Mobile, and TinyML workflows.
- Understand model compression techniques including quantization, pruning, and knowledge distillation.
- Learn how real-time inference is performed on IoT devices, mobile devices, and microcontrollers.
- Gain hands-on exposure to Edge AI workflows used in smart healthcare, robotics, agriculture, manufacturing, and smart cities.
Program Highlights
Mentor-Based Learning
Learn with guided support from mentors and domain experts.
Full e-LMS AccessAccess structured course materials, learning resources, and assessments.
Real-World Dry Lab ProjectsWork on practical Edge AI deployment and embedded AI use cases.
1:1 Project GuidanceReceive personalized guidance for practical and project-oriented learning.
Industry-Relevant ToolsExplore tools used in Edge AI, TinyML, embedded AI, and IoT-based deployment.
e-Certificate & e-MarksheetEarn certification after successful completion of learning activities and evaluation.
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Exposure to Edge AI deployment tools and workflows
- Self-assessment and final exam
- e-Certificate and e-Marksheet
Course Curriculum
Module 1: Introduction to Edge AI
- Overview of Edge AI and edge computing concepts
- Difference between cloud AI, edge AI, and embedded AI
- Real-world applications of Edge AI in IoT, healthcare, automation, and smart devices
Module 2: Fundamentals of AI Model Deployment
- Understanding the AI model development and deployment lifecycle
- Preparing trained AI models for real-world deployment
- Challenges of deploying AI models on low-power and resource-limited devices
Module 3: Edge Devices and Hardware Platforms
- Introduction to edge devices, microcontrollers, sensors, and embedded systems
- Overview of Raspberry Pi, Arduino, ESP32, Jetson Nano, and mobile devices
- Choosing the right hardware platform for Edge AI applications
Module 4: Model Optimization for Edge Deployment
- Need for model optimization in Edge AI systems
- Introduction to quantization, pruning, and model compression
- Balancing accuracy, latency, memory usage, and power consumption
Module 5: TensorFlow Lite for Edge AI
- Introduction to TensorFlow Lite and lightweight AI deployment
- Converting trained models into TensorFlow Lite format
- Running optimized inference on mobile, embedded, and IoT devices
Module 6: TinyML and AI on Microcontrollers
- Introduction to TinyML and ultra-low-power machine learning
- Deploying simple AI models on microcontrollers and sensor-based systems
- Use cases in keyword spotting, anomaly detection, and sensor intelligence
Module 7: ONNX Runtime and Cross-Platform AI Deployment
- Introduction to ONNX and model interoperability
- Using ONNX Runtime for deploying AI models across different platforms
- Understanding cross-framework deployment for Edge AI applications
Module 8: Edge AI for Computer Vision Applications
- Deploying image classification and object detection models on edge devices
- Real-time computer vision using cameras and embedded AI platforms
- Applications in surveillance, robotics, agriculture, and quality inspection
Module 9: Edge AI for IoT and Sensor Data
- Using AI for real-time analysis of sensor and IoT data
- Edge-based anomaly detection, predictive maintenance, and event detection
- Integrating AI models with IoT workflows and connected devices
Module 10: Security, Privacy, and Performance in Edge AI
- Understanding privacy benefits of local AI inference
- Security challenges in connected edge and IoT devices
- Performance monitoring, power efficiency, and deployment reliability
Module 11: Capstone Project and Real-World Edge AI Workflow
- Designing an end-to-end Edge AI deployment workflow
- Optimizing and deploying a lightweight AI model for practical use
- Presenting project outcomes, deployment challenges, and improvement strategies
Tools, Techniques, or Platforms Covered
Edge AI Artificial Intelligence TensorFlow Lite ONNX Runtime PyTorch Mobile TinyML Model Optimization Quantization Pruning IoT Devices Embedded Systems Real-Time Inference
Real-World Applications
- Real-time object detection on cameras and embedded vision devices
- AI-powered predictive maintenance for industrial IoT systems
- Smart healthcare devices for local monitoring and intelligent alerts
- Edge-based agriculture monitoring using sensors and image analysis
- AI-enabled robotics, drones, and autonomous systems
- Smart home automation using voice, motion, and sensor-based AI
- Privacy-preserving AI applications that process data locally on devices
Who Should Attend & Prerequisites
This program is suitable for learners and professionals interested in deploying AI models on real-world devices, embedded systems, IoT platforms, and intelligent edge hardware.
- AI and machine learning learners
- Engineers and developers working with IoT or embedded systems
- Students interested in Edge AI, TinyML, and smart device development
- Researchers and PhD scholars working on applied AI and intelligent systems
- Professionals in automation, robotics, smart manufacturing, and smart cities
- Data science and software professionals interested in AI deployment workflows
Outcomes
- Understand the complete workflow of deploying AI models on edge devices.
- Learn how to optimize AI models for memory, speed, latency, and power efficiency.
- Gain familiarity with TensorFlow Lite, ONNX Runtime, PyTorch Mobile, and TinyML concepts.
- Understand how Edge AI is applied in IoT, healthcare, robotics, agriculture, and smart infrastructure.
- Develop the ability to plan and evaluate real-world Edge AI deployment pipelines.
- Build confidence in applying Edge AI techniques to practical and research-oriented projects.
Certification
