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Articles about AI, machine learning, data science, and everything I'm learning while building real-world projects.
High accuracy scores fool people. Statistical thinking is what separates models that work in production from models that only worked in the notebook.
How I built a SaaS platform that turns raw CSV/Excel data into comprehensive statistical reports with just a few clicks.
ML models are not magic. Behind every good model is a foundation of statistical thinking that separates production-ready systems from academic experiments.
A Jupyter notebook is not a production system. Here's what I learned deploying real ML applications.