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(Draft) ML in Production — What Changes After You DeployLife after deploy — the exam never ends, overfitting vs leakage, data and concept drift, model decay, and picking metrics that match the money
21 min read en - views- - (Draft) ML Refresher — Revisit ML Before Production
Set up Python, venv, Jupyter, then revisit ML before production — baselines, feature schemas, train/serve skew, drift, and business-aligned metrics
18 min read en - views- - (Draft) Serving Machine Learning Models in Production
Take Machine Learning to production — model serving, the serving lifecycle, model decay, monitoring, and metrics for real-world use
24 min read en - views-