IND-104
Edge AI and Real-Time Inference
A two-day program on deploying AI models to edge devices for real-time industrial inference and control.
2 days
AI engineers, IoT engineers, embedded systems engineers, technical architects
advanced
hybrid
Coming SoonOverview
Course Overview
Many industrial AI applications require real-time inference at the edge, where latency, connectivity, or bandwidth constraints make cloud-based inference impractical. This program covers edge AI architecture, model optimization and compression, edge hardware selection, and deployment strategies for industrial environments.
Target Audience
AI engineers, IoT engineers, embedded systems engineers, technical architects
Prerequisites
IND-101 or equivalent. Programming and basic ML knowledge.
Learning Objectives
- Design edge AI architectures for industrial use cases
- Optimize and compress models for edge deployment
- Select appropriate edge hardware
- Deploy and manage models on edge devices
- Handle edge-to-cloud AI pipelines
Key Modules
- Edge AI Architecture and Use Cases
- Model Optimization and Quantization
- Edge Hardware Selection and Comparison
- Deployment on Edge Devices
- Edge-to-Cloud AI Pipelines
- Real-Time Inference and Control Systems
- Case Studies in Industrial Edge AI
Learning Outcomes
- An edge AI architecture design
- Practical skills in model optimization for edge
- Understanding of edge hardware options
- A framework for edge-to-cloud AI pipelines
Instructor
Your Instructor
Dr. Hussein Mustapha
AITECH Academy Faculty
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