Aim

This program aims to equip professionals and researchers with practical skills to deploy AI models on edge devices, enabling real-time processing and decision-making without reliance on cloud infrastructure. Participants will learn to optimize AI models for low-power environments and develop secure, scalable edge AI solutions.

Program Objectives

Learn Edge AI Fundamentals: Understand the core concepts and applications of edge AI.
Optimize AI Models for Edge Deployment: Gain proficiency in techniques to compress and optimize AI models for edge devices.
Work with AI Development Tools: Master tools like TensorFlow Lite, ONNX, and PyTorch Mobile for edge deployments.
Develop Real-Time AI Solutions: Build and deploy AI models for real-time data processing on devices like Raspberry Pi, NVIDIA Jetson, and more.
Address Security and Privacy: Understand the challenges and solutions for secure AI deployments at the edge.

Program Structure

Module 1: Introduction to Edge AI

Overview of Edge AI: Concepts and applications in various industries.
Benefits of AI at the edge vs. cloud AI.
Use cases in smart cities, healthcare, autonomous systems, and IoT.

Module 2: Edge AI Architectures and Devices

Overview of edge devices: Raspberry Pi, NVIDIA Jetson, Google Coral, Qualcomm AI Engine.
Understanding system architectures for edge AI.
Managing hardware constraints: Memory, power, and processing limits.

Module 3: Building and Optimizing AI Models for Edge Devices

Model compression techniques: Pruning, quantization.
Knowledge distillation for lightweight models.
Frameworks for edge AI development: TensorFlow Lite, PyTorch Mobile, OpenVINO.

Module 4: Convolutional Neural Networks (CNNs) for Edge AI

Lightweight CNN architectures: MobileNet, SqueezeNet, EfficientNet.
Real-time image processing on edge devices.
Optimizing CNNs for mobile and IoT applications.

Module 5: Recurrent Neural Networks (RNNs) and NLP on Edge Devices

Deploying NLP models like BERT and GPT on edge devices.
AI for speech recognition and real-time language translation.
Practical applications in voice assistants and real-time language processing.

Module 6: Model Deployment on Edge Devices

Deploying AI models with TensorFlow Lite, ONNX Runtime.
Edge AI deployment on NVIDIA Jetson and OpenVINO.
Real-world application deployment on Raspberry Pi and mobile devices.

Module 7: Real-Time Inference and Streaming on Edge

Real-time video and image analytics on edge devices.
Object detection and tracking in real-time environments.
Streaming data processing with AI at the edge.

Module 8: Edge AI for IoT and Smart Devices

Integrating AI with IoT networks for smart devices.
Use cases: Smart homes, wearables, Industry 4.0.
Deploying AI in low-resource IoT environments.

Module 9: Security and Privacy in Edge AI

Privacy concerns with AI at the edge.
Secure deployment and data encryption for edge devices.
Federated learning for privacy-preserving AI solutions.

Module 10: Energy Efficiency and Power Management for Edge AI

Techniques for power-efficient AI inference.
Managing resource constraints: Battery, CPU, and GPU limitations.
Using low-power AI hardware for edge applications.

Module 11: Edge AI in Autonomous Systems

AI for drones, self-driving cars, and robots.
Real-time decision-making in autonomous systems using edge AI.
Challenges and case studies in Edge AI deployment.

Final Project

Develop and deploy an AI model on an edge device.
Example: Build a real-time object detection system on Raspberry Pi or NVIDIA Jetson for smart surveillance or smart home applications.

Participant Eligibility

Data Scientists: Looking to deploy AI models in hardware-constrained environments.
AI Engineers: Focused on building efficient AI models for real-time use cases.
Embedded Systems Developers: Interested in integrating AI into low-power edge devices.
Researchers: Working on AI applications for autonomous systems or smart devices.

Program Outcomes

Edge AI Deployment Skills: Master the ability to deploy and manage AI models on edge devices.
Model Optimization: Proficiency in optimizing AI models for memory and processing constraints.
Real-Time AI Processing: Ability to enable real-time decision-making on hardware-constrained environments.
Security and Privacy Awareness: Deep understanding of privacy and security challenges in edge AI deployments.

Program Deliverables

Access to e-LMS: Complete access to all course materials online.
Real-Time Projects: Develop and deploy AI projects on edge devices.
Project Guidance: Mentorship and support through the project development process.
Research Paper Opportunity: Support for publishing work related to AI deployment on edge systems.
Final Examination: Certification awarded based on assignments, project reports, and exams.
e-Certification: Upon successful completion of the program, receive an e-Certificate.

Future Career Prospects

Edge AI Engineer: Focus on developing AI-driven applications for low-power devices.
Embedded AI Developer: Specialize in deploying AI on hardware-constrained environments.
IoT AI Specialist: Build intelligent IoT systems powered by edge AI.
AI Systems Architect: Design and implement scalable AI solutions for smart devices.
AI Solution Engineer for Smart Devices: Develop AI-driven products for industries like healthcare, smart homes, and manufacturing.
AI Innovation Specialist for Edge Computing: Lead AI innovation for edge computing applications.

Job Opportunities

Edge AI Engineer: Focus on developing AI applications for edge devices.
Embedded AI Developer: Implement AI models in embedded systems.
IoT AI Specialist: Integrate AI into IoT systems for smart devices.
AI Systems Architect: Design AI systems for low-power, real-time processing.
AI Solution Engineer for Smart Devices: Build AI solutions for industries like smart homes, healthcare, and autonomous systems.
AI Innovation Specialist: Lead development in edge computing and AI-driven products.

MODE	Online/ e-LMS
TYPE	Self Paced
LEVEL	Moderate
DURATION	3 Weeks

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Edge AI: Deploying AI on Edge Devices Course – 3 Weeks

Aim

Program Objectives