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

Reviews

There are no reviews yet.

Be the first to review “Edge AI: Deploying AI on Edge Devices Course”

Achieve Excellence & Enter the Hall of Fame!

Elevate your research to the next level! Get your groundbreaking work considered for publication in prestigious Open Access Journal (worth USD 1,000) and Opportunity to join esteemed Centre of Excellence. Network with industry leaders, access ongoing learning opportunities, and potentially earn a place in our coveted

Hall of Fame.

Achieve excellence and solidify your reputation among the elite!

AI

Edge AI: Deploying AI on Edge Devices Course

Aim

Program Objectives