Data Science and Machine Learning

Make the decision, estimand, data-generating process, and validation boundary explicit before selecting a method or model. Analytical validity outranks algorithm novelty.

Frame the problem

Define the decision or product behavior, unit of analysis, target or estimand, population, time horizon, cost of errors, operational constraints, and baseline. Determine whether the task is descriptive, predictive, causal, ranking, generation, detection, or optimization.

Load the relevant reference:

• data quality, statistics, experiments, and causal questions: data-and-experiments.md;
• features, models, validation, and error analysis: modeling-and-evaluation.md;
• reproducible training, accelerators, fine-tuning, and distributed scale: training-and-scale.md;
• serving, monitoring, governance, and retraining: deployment-and-monitoring.md.

Build an evidence chain