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Productionizing ML Models: Batch Scoring, Drift, and Pipelines

Book: Data Science: The Hard Parts
Author: Daniel Vaughan
ISBN: 978-1-098-14647-4

Chapter 11 ended with a line that sticks: in ML, the scoring stage reigns. Chapter 12 is the follow-through. Vaughan gives data scientists a helicopter view of what “production ready” actually means, because at many companies, there is no dedicated MLOps team. You built the model. You ship it too.

Data Leakage in ML: Why Your Test AUC Lies to You

Book: Data Science: The Hard Parts
Author: Daniel Vaughan
ISBN: 978-1-098-14647-4

If you have trained enough real models, you have probably hit data leakage. Vaughan thinks it should rank higher than the “top 10 problems” lists give it credit for. Chapter 11 is entirely about this: what leakage looks like, how to spot it, and how to build habits that prevent it.

Linear Regression Basics: What OLS Still Teaches About ML

Book: Data Science: The Hard Parts
Author: Daniel Vaughan
ISBN: 978-1-098-14647-4

Linear regression is the first algorithm most of us learn and the first one we abandon. Gradient boosting eats its lunch on predictive accuracy. Fair enough. But Vaughan’s Chapter 10 makes a case that skipping OLS intuition leaves you blind to problems that hit random forests and boosting just as hard.

Simulation and Bootstrapping: Testing ML Algorithms Before Real Data Bites

Book: Data Science: The Hard Parts
Author: Daniel Vaughan
ISBN: 978-1-098-14647-4

Most data science courses teach you on real datasets. Daniel Vaughan spends Chapter 9 asking a different question: what if you built the data yourself? Not synthetic data from a generative model. A hand-written data generating process (DGP) where you control every assumption. That shift sounds academic until your gradient boosting model looks brilliant on paper and falls apart in production.

Data Science: The Hard Parts - Why I'm Reading This Book

Book: Data Science: The Hard Parts
Author: Daniel Vaughan
ISBN: 978-1-098-14647-4
Publisher: O’Reilly Media (2024)

I’m starting a walkthrough of Daniel Vaughan’s Data Science: The Hard Parts. Over the next few weeks I’ll post one chapter at a time, retelling what the book says and adding my own take.

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