Description
You ship AI features, and the day one goes wrong you have nothing to say about why. You call models through an API and they feel like magic, which is fine until the magic is confidently wrong and someone asks you to fix it and you cannot, because you never learned what is happening underneath. You could not train a simple model of your own, read an evaluation metric and know whether it is lying to you, or tell why a model that aced your test set falls apart on real users. The whole field is built on machine learning, and you have been standing on top of it without ever learning the floor. Calling an API is not machine learning, it is using someone else’s. The scarce, best-paid work goes to the people who understand the model well enough to build it, break it, and trust it, and that understanding is exactly the floor you skipped.
This book teaches the machine learning foundation under the AI you already use, so you stop treating models as magic and start building ones you can explain, evaluate, and ship. It builds one system end to end, a churn predictor for a SaaS product, and uses it to teach what actually matters: what machine learning does that rules cannot, why the data problem decides everything before the model does, how to turn messy reality into features a model can learn from, and how to train a first model that beats a coin flip. It leans on the field’s standards, Aurelien Geron’s Hands-On Machine Learning and Chip Huyen’s Designing Machine Learning Systems, and refuses to stop at the notebook: reading evaluation metrics that lie and the ones that do not, why a model aces the training set and fails the real world, the classic algorithms that still win in production, serving predictions under latency, catching the drift that makes a model right in June and wrong by December, and designing the whole ML system around the model rather than just the model. For the engineer done calling AI a black box who wants the scarce, best-paid skill of understanding it.
Who should read this
This book is for: engineers who build AI features by calling models as black boxes, who can wire up an LLM but could not train, evaluate, or debug a model of their own, and who want the machine learning foundation that lets them understand why a model fails and ship one that works in production, not just a notebook.
The shortcut nobody hands you
The 6 Steps to Machine Learning That Works in Production, Not Just a Notebook. The foundation that turns calling models as black boxes into understanding, building, and shipping machine learning you can explain and trust. The whole AI field stands on ML, and the scarce, best-paid work goes to the people who know the floor, not just the API. Learn the data, the model, the metrics that lie, and the system around it, and ship ML that holds up after the notebook.
- See what ML does that rules can’t
- Fix the data problem before the model
- Turn reality into features a model can learn
- Read the metrics that lie, and the ones that don’t
- Beat overfitting; ship the model that generalizes
- Design the whole ML system, not just the model
Everything inside
- Chapter 1: The model I shipped that I couldn’t explain when it was wrong
- Chapter 2: What machine learning actually does that rules can’t
- Chapter 3: The data problem that decides everything before the model
- Chapter 4: Turning messy reality into features a model can learn
- Chapter 5: Training a first model that beats a coin flip
- Chapter 6: The accuracy number that lies, and the metrics that don’t
- Chapter 7: Why it aces the training set and fails the real world
- Chapter 8: The classic algorithms that still win in production
- Chapter 9: From a notebook to a system that serves predictions
- Chapter 10: The model that was right in June and wrong by December
- Chapter 11: Designing the whole ML system, not just the model
- Chapter 12: The engineer who ships ML that holds up
