5G and Edge AI: Enabling Ultra-Low Latency Applications

• Basic understanding of 5G network architecture
• Familiarity with AI and machine learning concepts
• Experience with edge computing and IoT applications

Audience

• Telecom professionals
• AI engineers
• IoT specialists

5G and Edge AI are transforming industries by enabling ultra-low latency applications for real-time decision-making and automation.

This instructor-led, live training (online or onsite) is aimed at intermediate-level telecom professionals, AI engineers, and IoT specialists who wish to explore how 5G networks accelerate Edge AI applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of 5G technology and its impact on Edge AI.
• Deploy AI models optimized for low-latency applications in 5G environments.
• Implement real-time decision-making systems using Edge AI and 5G connectivity.
• Optimize AI workloads for efficient performance on edge devices.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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