{"skill":{"slug":"afrexai-ml-engineering","displayName":"ML Engineering","summary":"Provides end-to-end methodology for defining, engineering, experimenting, deploying, and operating production ML/AI systems at scale.","tags":{"ai":"1.0.0","data-science":"1.0.0","latest":"1.0.0","machine-learning":"1.0.0","ml":"1.0.0"},"stats":{"comments":0,"downloads":556,"installsAllTime":0,"installsCurrent":0,"stars":0,"versions":1},"createdAt":1771504209669,"updatedAt":1777525247083},"latestVersion":{"version":"1.0.0","createdAt":1771504209669,"changelog":"Initial release: Complete, structured ML & AI engineering methodology for experiment-to-production workflows.\n\n- Provides a phased ML system design: problem framing, data engineering, experiment management, and more.\n- Includes YAML/spec templates for problem briefs, experiment tracking, and feature store design.\n- Offers checklists and practical guides for data quality scoring, leakage prevention, feature engineering, and model selection.\n- Features side-by-side ML vs rule-based decision criteria and kill criteria for ML efforts.\n- Curates algorithm recommendations and hyperparameter tuning strategies for major ML tasks.","license":null},"metadata":null,"owner":{"handle":"1kalin","userId":"s17e1q0nx23qnh4n429zzqc05x83hvsw","displayName":"1kalin","image":"https://avatars.githubusercontent.com/u/15705344?v=4"},"moderation":null}