Notes on machine learning
Technical notes written while learning: honest evaluation, data leakage, deployment, LLM applications and the mistakes behind each lesson. They come from the projects on this site, with the code one click away.
Your eval dropped from 90 to 89%: real regression or noise?
A new model scores 89.4% where the old one scored 90.0% on 1,000 examples. It looks like a regression. At that sample size it is noise. How to tell them apart before blocking a deploy.
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Leakage between train and test: the mistake that inflates every metric
A row that appears in both training and test makes the model look better than it is. It is easy to introduce by accident and hard to see by eye. Where it creeps in and how to catch it before you trust your numbers.
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From notebook to an inference API that survives production
A model trained in a notebook is not a product. Turning it into a service that validates its input, responds with predictable latency and can be monitored is the work that separates a demo from a system.
Read articleWhen is it worth using an LLM — and when is it not?
LLMs solve certain problems better than anything else. Others they solve worse than a three-line classifier. The question to ask before opening the OpenAI API.
Read articleHow to evaluate whether an ML model actually works
A model with 95% accuracy can be completely useless. A model that looks worse in validation may be the one that actually works in production. What separates honest evaluation from the kind that inflates results.
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Why the decision threshold matters more than the model
Moving the decision threshold from 0.5 to the right value for your business cost can improve the operational result more than switching from logistic regression to XGBoost. Why nobody explains it that way.
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RAG: verifiable answers over your documents
How retrieval-augmented generation works: indexing, semantic retrieval, chunking and evaluation. Why every answer should be able to cite where it comes from …
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MLOps: the difference between a model in a notebook and one in production
A notebook is an exploration environment, not a system. What it takes for a model to work in production, maintain itself and not degrade silently.
Read articleDrift: how to detect that your model is degrading
The model does not change. The data does. And if nobody is watching, the model can be giving bad predictions for weeks without anyone knowing. How to monitor drift in practice.
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