```html AI Architecture Fundamentals

Architecture Patterns
For AI Systems

Modern architectural approaches to building scalable, maintainable, and efficient AI-powered applications in production environments.

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Modern Design Patterns

Input Layer
Data Prep
Model Layer
Processing
Postprocess
Output

Pipeline Architecture

Sequential processing layers for feature extraction, transformation, and decision-making in AI workflows.

Modular design patterns
Scalable for cloud/on-prem
Real-time inference

Implementation Example 

// Example ML pipeline using TensorFlow
const model = tf.sequential();
model.add(tf.layers.dense({inputShape: [inputSize], units: hiddenSize, activation: 'relu'}));
model.add(tf.layers.dropout({rate: 0.5}));
model.add(tf.layers.dense({units: outputSize, activation: 'softmax'}));

model.compile({
  loss: 'categoricalCrossentropy',
  optimizer: tf.train.adam(learningRate),
  metrics: ['accuracy']
});
TensorFlow Batch Processing Node.js

Design Best Practices

Modular Components

Break architectures into reusable, testable components that can evolve independently through CI/CD.

Decoupled deployment units

Async Pipelines

Leverage parallel processing and message queues to handle high-throughput AI workloads efficiently.

Scalable to any traffic
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