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Duration 21 hours
Course Outline
Introduction to 5G and Edge AI
- Overview of 5G networks and edge computing
- Distinct advantages of 5G over 4G for AI applications
- Challenges and prospects in ultra-low latency AI
5G Architecture and Edge Computing
- Comprehending 5G network slicing for AI workloads
- The significance of Multi-Access Edge Computing (MEC)
- Strategies for deploying Edge AI in telecom settings
Deploying AI Models on Edge Devices with 5G
- Leveraging TensorFlow Lite and OpenVINO for Edge AI
- Fine-tuning AI models for real-time processing
- Case study: AI-driven video analytics over 5G
Ultra-Low Latency Applications Powered by 5G
- Autonomous vehicles and intelligent transport systems
- AI-based predictive maintenance in industrial contexts
- Medical applications: remote diagnostics and continuous monitoring
Security and Reliability in 5G Edge AI Systems
- Data privacy and cybersecurity issues in 5G AI
- Maintaining AI model robustness in real-time scenarios
- Regulatory adherence for AI-enhanced telecom solutions
Future Trends in 5G and Edge AI
- Progress in 6G and AI-driven networking
- Combining federated learning with 5G AI
- Emerging applications in smart cities and IoT
Conclusion and Pathways Forward
Requirements
- Foundational knowledge of 5G network architecture
- Familiarity with AI and machine learning principles
- Practical experience with edge computing and IoT applications
Target Audience
- Telecom professionals
- AI engineers
- IoT specialists
Testimonials (1)
That we can cover advance topic and work with real-life example