#mlops
5 articles
01
·Machine Learning Basics·★ MEMBER·8 min read
ML System Design — The 90% Outside the Model
The accuracy you hit in a notebook is not a promise about production. Feature definitions, training-serving skew, monitoring that catches slow decay, and the retraining loop — the 90% that lives outside the model, laid out in the order you actually design it.
02
·Machine Learning Basics·★ MEMBER·8 min read
ML System Design — The 90% Outside the Model
The accuracy you hit in a notebook is not a promise about production. Feature definitions, training-serving skew, monitoring that catches slow decay, and the retraining loop — the 90% that lives outside the model, laid out in the order you actually design it.
03
·Training & Alignment·★ MEMBER·10 min read
Versioning Data and Models — An Experiment You Cannot Reproduce Never Happened
If you cannot get "92% accuracy" back six months later, it was an anecdote, not an experiment. We build up content-addressing (naming things by their contents), lineage (the graph from inputs to outputs), and the three levels of "how much is worth pinning down" — from the hash math to a manifest you can write today to the mistakes that quietly ruin a year of runs.
04
·Training & Alignment·★ MEMBER·10 min read
Versioning Data and Models — An Experiment You Cannot Reproduce Never Happened
If you cannot get "92% accuracy" back six months later, it was an anecdote, not an experiment. We build up content-addressing (naming things by their contents), lineage (the graph from inputs to outputs), and the three levels of "how much is worth pinning down" — from the hash math to a manifest you can write today to the mistakes that quietly ruin a year of runs.
05
·Machine Learning Basics·★ MEMBER·10 min read
Data Leakage and Experiment Hygiene — When the Score Is Too Good, Suspect It
When a model scores better than you expected, that is not good news yet. This article splits data leakage into five concrete shapes — outcome-derived columns, preprocessing before the split, time running backwards, duplicates and groups, and a worn-out test set — then covers the detection moves that actually find them and the experiment records that let you dissect the day the number jumped.