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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

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