From dataset to deployment: An end-to-end QA checklist for data scientists

This guide addresses the crucial need for quality assurance (QA) in modern data science, especially for complex AI agent systems. Traditional QA methods are inadequate for the non-deterministic nature of AI; therefore, new frameworks are necessary. The guide presents a comprehensive QA checklist, spanning data quality assurance, model development QA, and evaluation beyond accuracy. Data QA focuses on representativeness, temporal order, bias mitigation, and data lineage. Model development emphasizes testable components, quantifying uncertainty, and formal specifications. Evaluation includes adversarial testing, fairness assessments, and explanation quality. Pre-deployment validation involves pipeline testing, shadow mode deployment, and extreme condition testing. Finally, the guide stresses ongoing production monitoring and continuous QA to ensure reliability as data and model behavior evolve.