From Notebook to Product: Shipping Machine Learning

The gap between a working Jupyter notebook and a production ML system is vast. I've seen brilliant models fail in production and mediocre models succeed. The difference? Engineering discipline.

The Notebook Trap

Notebooks are great for exploration, terrible for production. They encourage:

Hidden state and execution order dependencies

Lack of version control and reproducibility

Mixing of concerns (data processing, training, evaluation)

No testing or error handling

The first step to production is getting out of the notebook.

Data Contracts: The Foundation

Before writing any model code, establish data contracts:

**Schema validation**: What fields exist, what types, what ranges?

**Quality checks**: Missing values, outliers, distribution shifts

**Versioning**: Track data lineage and changes over time

Use tools like Great Expectations or Pandera. Fail fast when data doesn't match expectations.

Model Development Pipeline

Structure your code like software:

**Data loading**: Separate module, versioned, tested

**Feature engineering**: Pure functions, unit tested

**Training**: Reproducible, logged, checkpointed

**Evaluation**: Multiple metrics, validation sets, error analysis

Use MLflow or Weights & Biases to track everything.

The Evaluation Problem

Accuracy isn't enough. Ask:

How does it perform on edge cases?

What's the latency at scale?

How does it degrade with data drift?

What happens when it's wrong?

Build evaluation suites that test these scenarios.

Deployment Strategies

Start simple:

**Batch predictions**: Easiest, works for many use cases

**API serving**: Use FastAPI or similar, containerize

**Real-time**: Only if you actually need it

Monitor everything: latency, throughput, error rates, data drift, model performance.

Feedback Loops

The model you deploy is just the beginning. Build systems to:

Collect user feedback (implicit and explicit)

Detect performance degradation

Retrain automatically or semi-automatically

A/B test model versions

The Unglamorous Truth

90% of ML engineering is data pipelines, monitoring, and infrastructure. 10% is the actual model. Embrace this reality. The teams that succeed are those that treat ML as software engineering with a statistical component, not magic.

Ship early, measure everything, iterate constantly. That's how you go from notebook to product.