AI on the Edge - Cloud

Overview

Explore the evolution of cloud computing with AI on the edge, focusing on pushing logic and ML models from Azure to edge applications using IoT Edge and its custom modules.

Syllabus

• Introduction to AI on the Edge
Overview of Cloud Computing
Introduction to Edge Computing
Benefits and Challenges of AI on the Edge
• Fundamentals of IoT Edge
Understanding IoT Architectures
IoT Edge vs. Cloud Computing
Overview of Azure IoT Edge
• Edge Computing with Azure IoT Edge
Setting up Azure IoT Edge
Managing Devices and Modules
Deploying Edge Solutions from Azure Marketplace
• Developing Custom Modules for IoT Edge
Introduction to Docker and Containerization
Building Custom Modules using Python/C#
Best Practices for Module Design
• Machine Learning Models on the Edge
Overview of Machine Learning in Edge Computing
Deploying Pre-trained Models to IoT Edge Devices
Training and Tuning Models in Low-Resource Environments
• Case Studies and Use Cases
Real-world Examples of AI on the Edge
Industry-specific Applications (Manufacturing, Healthcare, Agriculture)
Success Stories and Lessons Learned
• Security and Compliance in Edge AI
Addressing Security Issues in Edge Deployments
Data Compliance and Privacy Concerns
Ensuring End-to-End Security
• Monitoring and Managing Edge Devices
Implementing Telemetry and Monitoring
Troubleshooting Common Issues
Scaling Edge Solutions and Maintaining Performance
• Future Trends and Innovations in Edge AI
Emerging Technologies and Opportunities
The Role of 5G and Enhanced Connectivity
Preparing for the Future of Edge AI
• Course Review and Project Showcase
Recap of Key Learnings
Hands-on Project Presentations
Feedback and Future Learning Paths

Subjects

Conference Talks