Build your first tiny AI pipeline

Connecting raw input, string normalization, numerical feature extraction, linear model scoring, and decision boundary thresholding into a transparent, observable pipeline.

Real-World Mental Model

“Building a miniature wooden waterwheel before constructing a hydro-electric Hoover Dam.”

INTERACTIVE ENGINE: PIPELINE STEPPER

Live Reactive

Pipeline Input String:

Stage 1: Text Sanitization & Lowercasing

Cleaned String: “this ai engineering curriculum is incredibly fast and clear”

Removes punctuation noise and standardizes letter casing to ensure consistent vocabulary mapping.

Step 1 of 4

CORE PRINCIPLES

What You Must Understand

Pipeline: Input ➔ Feature Extractor ➔ Model Math ➔ Threshold Decision.

Every intermediate mathematical state must be logged and inspectable.

Establishes the architecture for complex neural network and RAG pipelines.

Reference Implementation

export function tinyPipeline(rawText: string, weight = 1.4, bias = -0.3) {
  const charLength = rawText.trim().length;
  const x = Math.min(1, charLength / 100); // Feature extraction
  const z = (weight * x) + bias;          // Model parameter math
  const probability = 1 / (1 + Math.exp(-z)); // Sigmoid activation
  return { input: rawText, x, z, probability, prediction: probability > 0.5 ? 'POSITIVE' : 'NEGATIVE' };
}

Production Failure Mode

Failing to log intermediate states, making pipeline debugging an exercise in guesswork.

View Full 26-Topic Curriculum Table

Loading AI Engineering Foundations...

export function tinyPipeline(rawText: string, weight = 1.4, bias = -0.3) { const charLength = rawText.trim().length; const x = Math.min(1, charLength / 100); // Feature extraction const z = (weight * x) + bias; // Model parameter math const probability = 1 / (1 + Math.exp(-z)); // Sigmoid activation return { input: rawText, x, z, probability, prediction: probability > 0.5 ? 'POSITIVE' : 'NEGATIVE' }; }