Building Production ML Pipelines

Most ML tutorials focus on model training. The harder part is everything else: getting data in, keeping models healthy, and catching problems before users do.

The Reality of Production ML

Training a model is maybe 10% of the work. The rest is:

Data pipelines that don't break
Monitoring that catches drift
Tests that actually test something
Deployment that doesn't require a PhD

Key Principles

1. Start with Monitoring

Before you deploy anything, know how you'll answer "is it working?" Define metrics upfront:

Prediction latency (p50, p95, p99)
Input data distributions
Output distributions
Error rates by category

2. Test Like You Mean It

Unit tests for ML are tricky but necessary:

def test_model_output_shape():
    model = load_model()
    input_data = create_test_input()
    output = model.predict(input_data)
    assert output.shape == (1, NUM_CLASSES)

def test_model_deterministic():
    model = load_model()
    input_data = create_test_input()
    out1 = model.predict(input_data)
    out2 = model.predict(input_data)
    assert np.allclose(out1, out2)

3. Version Everything

Not just code—data, models, configs. When something breaks at 3am, you need to know what changed.

Drift Detection

Models degrade silently. Input distributions shift, user behavior changes, the world moves on. Build drift detection into your pipeline from day one.

Simple approach: track statistical properties of inputs and outputs. Alert when they diverge from training distributions.

Conclusion

Production ML is software engineering with extra uncertainty. Treat it that way: test, monitor, version, and automate.