5G and Edge AI: Enabling Ultra-Low Latency Applications Training Course
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.
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
- 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
Open Training Courses require 5+ participants.
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