# Online Learning Systems

> Use when building models that learn incrementally.

- Skill: `loopyluci/online-learning-systems` (Agent Skill)
- Install (CLI): `npx skillmds@latest add loopyluci/online-learning-systems`
- Raw SKILL.md: https://api.skillmd.com/api/skills/loopyluci/online-learning-systems/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: LoopyLuci (https://skillmd.com/u/loopyluci)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/loopyluci/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

```python
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