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Duration 21 hours
Course Outline
Introduction to Edge AI in Computer Vision
- Exploring the benefits and overview of Edge AI.
- A comparative analysis of Cloud AI versus Edge AI.
- Addressing the primary challenges in real-time image processing.
Deploying Deep Learning Models on Edge Devices
- Getting started with TensorFlow Lite and OpenVINO.
- Strategies for optimizing and quantizing models for edge deployment.
- Case study: Executing YOLOv8 on edge hardware.
Hardware Acceleration for Real-Time Inference
- Overview of edge computing hardware, including Jetson, Coral, and FPGAs.
- Maximizing performance through GPU and TPU acceleration.
- Methods for benchmarking and evaluating performance.
Real-Time Object Detection and Tracking
- Implementing object detection using YOLO models.
- Tracking moving objects with real-time precision.
- Improving detection accuracy through sensor fusion techniques.
Optimization Techniques for Edge AI
- Reducing model footprint via pruning and quantization.
- Strategies to minimize latency and power consumption.
- Retraining and fine-tuning Edge AI models for better adaptation.
Integrating Edge AI with IoT Systems
- Deploying AI models on smart cameras and various IoT devices.
- Enabling real-time decision-making with Edge AI.
- Managing communication between edge devices and cloud infrastructure.
Security and Ethical Considerations in Edge AI
- Navigating data privacy concerns in Edge AI applications.
- Securing models against adversarial attacks.
- Ensuring compliance with AI regulations and upholding ethical AI principles.
Summary and Future Directions
Requirements
- A solid understanding of fundamental computer vision concepts.
- Practical experience with Python and deep learning frameworks.
- Foundational knowledge of edge computing architectures and IoT devices.
Target Audience
- Computer vision engineers.
- AI developers.
- IoT professionals.
Testimonials (1)
That we can cover advance topic and work with real-life example
