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Course Outline
Fundamentals of 5G and Edge AI
- An overview of 5G networks and edge computing paradigms
- Critical distinctions between 4G and 5G regarding AI applicability
- Navigating the challenges and opportunities in ultra-low latency AI
5G Architecture and Edge Computing Integration
- Exploring 5G network slicing for dedicated AI workloads
- The function of Multi-Access Edge Computing (MEC)
- Strategies for Edge AI deployment in telecom landscapes
Implementing AI Models on Edge Devices via 5G
- Utilizing TensorFlow Lite and OpenVINO for Edge AI tasks
- Refining AI models for real-time processing speed
- Case study: AI-driven video analytics over 5G networks
Ultra-Low Latency Use Cases Powered by 5G
- Autonomous vehicles and intelligent transportation systems
- AI-based predictive maintenance for industrial operations
- Medical applications: remote diagnostics and continuous monitoring
Security and Reliability in 5G Edge AI Systems
- Addressing data privacy and cybersecurity risks in 5G AI
- Guaranteeing AI model robustness in live, real-time scenarios
- Meeting regulatory standards for AI-enabled telecom services
Emerging Trends in 5G and Edge AI
- Progress in 6G and AI-centric networking
- Combining federated learning with 5G AI frameworks
- Future applications in smart cities and the IoT ecosystem
Recap and Future Directions
Requirements
- A foundational grasp of 5G network architecture
- Working knowledge of AI and machine learning principles
- Practical experience with edge computing and IoT applications
Target Audience
- Telecom professionals
- AI engineers
- IoT specialists
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example