Edge AI: Deploying AI on Edge Devices
Bring AI to the Edge: Real-Time, On-Device AI for Faster, Smarter Solutions
Skills you will gain:
Edge AI refers to running AI models directly on edge devices such as smartphones, sensors, and IoT devices, enabling faster inferences and reducing data transmission to the cloud. This course covers AI deployment on hardware-constrained devices, model optimization techniques, and tools like TensorFlow Lite and ONNX.
Aim: This program aims to teach professionals and researchers the practical skills needed to deploy AI models on edge devices, enabling real-time processing and decision-making without reliance on cloud infrastructure.
Program Objectives:
- Understand the fundamentals of Edge AI and its applications.
- Learn techniques for optimizing AI models for deployment on edge devices.
- Gain proficiency in tools like TensorFlow Lite, ONNX, and PyTorch Mobile.
- Develop and deploy AI solutions for real-time processing on edge devices.
- Address security and privacy challenges in edge computing environments.
What you will learn?
- Introduction to Edge AI
- Overview of Edge AI: Concepts and Applications
- Benefits of AI at the Edge vs. Cloud AI
- Use Cases in Smart Cities, Healthcare, Autonomous Systems, and IoT
- Edge AI Architectures and Devices
- Edge Devices Overview: Raspberry Pi, NVIDIA Jetson, Google Coral, Qualcomm AI Engine
- System Architectures for Edge AI
- Hardware Constraints: Memory, Power, and Processing Limits
- 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
- 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
- Recurrent Neural Networks (RNNs) and NLP on Edge Devices
- Deploying NLP Models (BERT, GPT) on Edge Devices
- Speech Recognition and Processing on Edge
- Applications in Real-Time Language Translation and Assistants
- Model Deployment on Edge Devices
- Deploying AI Models with TensorFlow Lite and ONNX Runtime
- Edge AI Deployment with NVIDIA Jetson and OpenVINO
- Real-World Application Deployment on Raspberry Pi and Mobile Devices
- Real-Time Inference and Streaming on Edge
- Real-Time Video and Image Analytics on Edge
- Object Detection and Tracking with Edge Devices
- Streaming Data Processing with AI at the Edge
- Edge AI for IoT and Smart Devices
- Integration of AI with IoT Networks
- Use Cases in Smart Homes, Wearables, and Industry 4.0
- Deploying AI in Low-Resource IoT Environments
- Security and Privacy in Edge AI
- Privacy Concerns with AI at the Edge
- Secure Deployment and Data Encryption
- Federated Learning for Privacy-Preserving AI
- Energy Efficiency and Power Management for Edge AI
- Power-Efficient AI Inference
- Managing Resource Constraints (Battery, CPU, GPU)
- Low-Power AI Hardware for Edge Devices
- Edge AI in Autonomous Systems
- AI for Drones, Self-Driving Cars, and Robots
- Real-Time Decision-Making in Autonomous Systems
- Challenges and Case Studies in Edge AI Deployment
Intended For :
Data scientists, AI engineers, embedded systems developers, and researchers focused on deploying AI models on hardware-constrained devices.
Career Supporting Skills