"Correlation" by Randall Munroe, xkcd.com — CC BY-NC 2.5
Source: https://xkcd.com/552/
Stories and practical guides on using statistics to solve engineering problems, design high-value systems, and understand the statistical concepts that show up in real production scenarios.
From debugging rare failures to detecting model drift, see how engineers use statistics to solve real production problems efficiently.
Learn how statistical thinking helps you architect production systems—especially AI-based ones—that scale reliably and perform predictably.
Distributions, hypothesis testing, anomaly detection, and time-series analysis— explained through real engineering scenarios, not textbook theory.
I didn't want to give a bassinet camera feed to a vendor that might get acquired, sunset, or breached. So I built a local-only baby monitor on a Mac that was about to be e-waste, taught it to watch eye state with a small ML model, and learned more about ML deployment than my last three actual ML jobs.
Read article →A practical guide (with examples) to using statistical modeling to understand, predict, and prevent mysterious failures in modern distributed systems.
Read article →Most software engineers spend years sharpening their coding skills but go surprisingly far without understanding the statistical forces shaping their systems. Here are eight stories of engineers solving real problems with stats.
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