Domain Neighborhood
Production ML
The engineering discipline around trustworthy model use: evaluation pipelines, dataset and model versioning, monitoring, drift, reproducibility, and operational tradeoffs.
Recommended Route
Start here, then follow the prerequisites forward.
This sequence is ordered for learning rather than inventory: lower difficulty, fewer prerequisites, and more central concepts come first.
- 01Evaluation Pipelines
Evaluation pipelines freeze data, splits, metrics, contamination checks, and report artifacts so model comparisons stay trustworthy.
19 mincodedemoafter Model Selection and Hyperparameter Search, Classification Metrics, Thresholds, and Calibration, Train/Dev/Test Splits, Cross-Validation, and LeakageCheck Model Selection and Hyperparameter Search first if the symbols feel slippery.
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