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Duration 21 hours
Course Outline
Introduction to Edge AI for Computer Vision
- Overview of Edge AI and its key benefits.
- Comparative analysis: Cloud AI vs. Edge AI.
- Identifying primary challenges in real-time image processing.
Deploying Deep Learning Models on Edge Devices
- Introduction to TensorFlow Lite and OpenVINO frameworks.
- Optimizing and quantizing models specifically for edge deployment.
- Case study: Executing YOLOv8 on edge hardware.
Hardware Acceleration for Real-Time Inference
- Surveying edge computing hardware options (Jetson, Coral, FPGAs).
- Maximizing performance through GPU and TPU acceleration.
- Benchmarking strategies and performance evaluation metrics.
Real-Time Object Detection and Tracking
- Implementing object detection pipelines using YOLO models.
- Achieving real-time tracking of moving objects.
- Improving detection accuracy through sensor fusion techniques.
Optimization Techniques for Edge AI
- Reducing model footprint via pruning and quantization.
- Strategies for minimizing latency and power consumption.
- Retraining and fine-tuning Edge AI models for continuous improvement.
Integrating Edge AI with IoT Systems
- Deploying AI models on smart cameras and connected IoT devices.
- Enabling real-time decision-making capabilities via Edge AI.
- Managing communication protocols between edge devices and cloud systems.
Security and Ethical Considerations in Edge AI
- Addressing data privacy concerns within edge AI applications.
- Safeguarding models against adversarial attacks.
- Ensuring compliance with AI regulations and ethical principles.
Summary and Next Steps
Requirements
- Solid understanding of 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
