Introduction to AI engineering
summary
Argues that practical AI work is engineering: three real constraints — data scarcity, available models/algorithms and GPU training — decide what actually works. Describes roles that sit between papers and production: research scientist → research engineer → software/systems engineer, and a funnel from papers to GitHub implementations to running web or device apps. Demonstrates sharpening images with a neural model, proposes data augmentation to synthesize text training data, and recommends using pre-trained models where possible.
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