Edge AI Fundamentals Training in San Francisco, USA

This training introduces participants to Edge AI—the practice of running artificial intelligence algorithms directly on devices such as smartphones, sensors, IoT devices, and embedded systems. The course focuses on foundational concepts, hands-on tools, and real-world applications across industries like manufacturing, healthcare, automotive, and smart devices.

Foundations of Edge AI

What is Edge AI?

• Definition and key concepts
• Differences between cloud AI and edge AI
• Advantages: Low latency, privacy, offline use, real-time insights

Edge AI Architecture and Components

• AI pipelines on edge devices
• Hardware: Raspberry Pi, Coral Dev Board, smartphones
• Software stacks: TensorFlow Lite, PyTorch Mobile

AI Model Optimization and Conversion

• Reducing model size: quantization, pruning
• Converting models to run on edge (e.g., TFLite conversion)
• Example: Convert a CNN model for image classification

Applications and Deployment

Real-World Use Cases of Edge AI

• Smart cameras (face/mask detection)
• Industrial automation (fault detection)
• Healthcare devices (monitoring vitals)
• Smart homes (voice assistants, gesture control)

Building Lightweight AI Applications

• Create a basic AI app using TensorFlow Lite
• Use cases: Voice command recognition, anomaly detection
• Optimize for performance, memory, and battery usage

Edge AI Deployment Challenges

• Security and privacy
• Connectivity issues and offline usage
• Updating models remotely

Final Exercise – Design an Edge AI Solution

• Choose a scenario (e.g., smart farming, security, health monitoring)
• Define the problem, model type, device, and deployment flow
• Present a prototype or system design

Wrap-Up and Next Steps

• Open-source tools and kits for further learning
• How to integrate Edge AI into existing projects
• Final Q&A and feedback