Online Learning Systems

Online Learning Systems

Building ML models that learn incrementally from streaming data — from online gradient descent through bandit algorithms, streaming features, and production deployment.

When to Use

• Data arrives as a stream (real-time, high volume)
• Models must adapt to changing distributions quickly
• Training on all historical data is too expensive
• Building bandit systems for real-time optimization

Online Learning Algorithms

class OnlineSGD:
    """Online Stochastic Gradient Descent."""
    def __init__(self, n_features: int, lr: float = 0.01):
        self.weights = np.zeros(n_features)
        self.lr = lr
    
    def partial_fit(self, x: np.array, y: float):
        """Update model with one sample at a time."""
        pred = np.dot(self.weights, x)
        self.weights += self.lr * (y - pred) * x
    
    def predict(self, x: np.array) -> float:
        return np.dot(self.weights, x)

Common Pitfalls

1. Concept drift — online learning adapts slowly to sudden shifts
2. Catastrophic interference — new data can overwrite useful old patterns
3. No baseline — compare with periodically retrained batch model
4. Cold start — warm-start with mini-batch from historical data

Verification Checklist

• Algorithm supports incremental updates (partial_fit)
• Feature computation consistent in training and serving
• Concept drift detection integrated
• Model checkpointing for recovery