🔍 5 resources most ML people never stumble on

These are the things practitioners quietly rely on but rarely share.

1. Google's "Rules of Machine Learning"
43 numbered rules from Google engineers on when to add complexity, how to catch training/serving skew, and when a heuristic beats a model. Written from real production postmortems.

2. Chip Huyen's ML Systems Design notes
Free breakdown of how companies actually design ML systems: data pipelines, feature stores, serving latency, model monitoring. The stuff no ML course teaches.

3. Full Stack Deep Learning
Free course built on one premise: training the model is the easy 20%. Covers deployment, cost tradeoffs, data labeling, and how models fail in production.

4. alphaXiv
Same papers as arXiv, but with inline comment threads under each section, sometimes answered by the paper's own authors. Turns a static PDF into an ongoing discussion.

5. Sebastian Raschka's "Ahead of AI"
Newsletter that dissects specific architecture and training decisions (why this optimizer, why this attention variant) at a depth most blogs skip.