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Course Outline
Introduction to 5G and Edge AI
- Overview of 5G networks and edge computing.
- Key differences between 4G and 5G for AI applications.
- Challenges and opportunities in ultra-low latency AI.
5G Architecture and Edge Computing
- Understanding 5G network slicing for AI workloads.
- Role of Multi-Access Edge Computing (MEC).
- Edge AI deployment strategies in telecom environments.
Deploying AI Models on Edge Devices with 5G
- Using TensorFlow Lite and OpenVINO for Edge AI.
- Optimizing AI models for real-time processing.
- Case study: AI-powered video analytics over 5G.
Ultra-Low Latency Applications Enabled by 5G
- Autonomous vehicles and smart transportation.
- AI-driven predictive maintenance in industrial settings.
- Healthcare applications: remote diagnostics and monitoring.
Security and Reliability in 5G Edge AI Systems
- Data privacy and cybersecurity challenges in 5G AI.
- Ensuring AI model robustness in real-time applications.
- Regulatory compliance for AI-powered telecom solutions.
Future Trends in 5G and Edge AI
- Advancements in 6G and AI-driven networking.
- Integration of federated learning with 5G AI.
- Next-generation applications in smart cities and IoT.
Summary and Next Steps
Requirements
- Foundational knowledge of 5G network architecture.
- Familiarity with AI and machine learning concepts.
- Prior experience with edge computing and IoT applications.
Target Audience
- Telecom professionals.
- AI engineers.
- IoT specialists.
21 Hours
Testimonials (1)
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