# Chapter 01: How AI dey Change Deployment for Edge

EdgeAI na new way wey AI dey work, e dey move AI power from cloud-based processing go local edge devices. Dis chapter go talk about di basic ideas, di main technology, and di practical ways wey dis new style of AI dey work.

## Module Structure

### [Section 1: EdgeAI Basics](./01.EdgeAIFundamentals.md)
Dis section go explain di difference between di normal cloud-based AI and di edge AI deployment style. We go look di important technology like model quantization, compression optimization, and Small Language Models (SLMs) wey dey help edge devices manage di work wey dem suppose do. Di talk go show how dis new ideas dey give better privacy, very fast response time, and offline processing wey strong well.

### [Section 2: Real-Life Examples](./02.RealWorldCaseStudies.md)
With examples like Microsoft's Phi and Mu model systems and Japan Airlines' AI reporting system, dis section go show how EdgeAI dey work well for different industries. Di examples go prove how SLMs dey perform well for special tasks and how edge deployment dey bring better results.

### [Section 3: How to Do Am Yourself](./03.PracticalImplementationGuide.md)
Dis section go give full guide on how to prepare environment for learning, including di tools wey you need, hardware requirements, di main model resources, and optimization frameworks. E go help learners get di technical knowledge wey dem need to build and deploy their own EdgeAI solutions.

### [Section 4: Edge AI Deployment Hardware Platforms](./04.EdgeDeployment.md)
Dis section go talk about di hardware wey dey make edge AI deployment possible, like di platforms from Intel, Qualcomm, NVIDIA, and Windows AI PCs. E go compare di hardware power, di special optimization techniques for each platform, and di things wey you need to think about when you dey deploy edge computing.

## Wetin You Go Learn

By di end of dis chapter, readers go sabi:
- Di main difference between cloud AI and edge AI
- Di main techniques to optimize edge deployment
- Real-life examples and how dem dey work
- How to do EdgeAI solutions by yourself
- How to choose hardware platform and di special optimization for each one
- How to test performance and di best way to deploy am

## Wetin Go Happen for Future

EdgeAI na di big thing wey dey change how AI dey work, e dey bring distributed, efficient, and privacy-protecting AI systems wey fit work without cloud connection but still dey perform well.

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