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// bridge system
useState()→Model Weights·Event Propagation→Forward Pass·Array.map()→Tensor Operation·React diff→Loss Function L = Σ(y−ŷ)²·transition-duration→Learning Rate η·CSS clamp()→σ(x) Activation·Re-render cycle→Training Epoch·Event bubbling→Backpropagation ∂L/∂w·useCallback→Gradient Caching·Promise.all()→Batch Inference·Redux store→Weight Matrix·DevTools profiler→Loss Landscape·useState()→Model Weights·Event Propagation→Forward Pass·Array.map()→Tensor Operation·React diff→Loss Function L = Σ(y−ŷ)²·transition-duration→Learning Rate η·CSS clamp()→σ(x) Activation·Re-render cycle→Training Epoch·Event bubbling→Backpropagation ∂L/∂w·useCallback→Gradient Caching·Promise.all()→Batch Inference·Redux store→Weight Matrix·DevTools profiler→Loss Landscape·
Bridges/ML pipeline (data → train → deploy)
Structural Bridge

CI/CD pipeline
=
ML pipeline (data → train → deploy)

Terra Grid // Bridge #49
The connection

Both are automated multi-stage pipelines that transform inputs into production artifacts. CI/CD: code → build → test → deploy. ML: data → train → evaluate → deploy. Same workflow pattern.

Why "Structural"?

Structural bridges share the same architecture or pattern, even though the domains differ.

Frontend concept
CI/CD pipeline
View in glossary
ML concept
ML pipeline (data → train → deploy)
View in glossary
Related bridges in Terra Grid
CDN edge locations=Edge inference
Structural
Image format tradeoffs (PNG vs. WebP)=Model quantization
Structural
Tree-shaking=Weight pruning
Identity
Code splitting=Knowledge distillation
Structural
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