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From Notebook to Production

The engineering path from a model that works in a notebook to a system that runs reliably in production: deployment, monitoring, cost control, model routing, and the decisions that keep AI alive after the demo.

The lessons

  1. 1

    Part 1 · 7 min

    Your Model Works in a Notebook. That Is the Easy Part.

    A notebook proves a model can work once. Production requires it to work continuously, on unseen data, within a budget, and to fail safely. The five questions that define the gap, and why deployment is the scarce skill.

  2. 2

    Part 2 · 7 min

    Serving a Model: Endpoint, Batch, or Embedded?

    Three ways to serve a model, endpoint, batch, or embedded, and the one question that decides which. Most people default to the always-on endpoint. It is the priciest and most fragile option, and usually the wrong one.

  3. 3

    Part 3 · 10 min

    Your 0.85 Meant 0.98: A 20,000-Row Calibration Test That Caught a Top Model Misstating Its Own Odds

    On 20,000 rows, a random forest with AUC 0.972 had a calibration error of 0.082. Sigmoid calibration cut it to 0.004. Gaussian naive Bayes fell from 0.050 to 0.012 with isotonic calibration, and AUC moved by at most 0.001.