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Reproducibility is a foundational pillar of scientific progress, especially in computational and data-driven research. However, failures in reproducibility often stem from poorly documented computational environments, a challenge impacting organizations and research worldwide, including advanced tech implementers like MHTECHIN. 1. Understanding the Crisis: The Importance of Reproducibility 2. Why Do Undocumented Environments Cause Failures? a. Technical Factors b. Human…

Introduction In the world of data science, machine learning, and software engineering, dataset management is foundational to achieving reliable, reproducible results. Yet, a commonly overlooked pitfall is the silent danger of unversioned dataset overwrites—where new data simply replaces the old without retaining a history of changes or clear traceability. This can cause mysterious regressions in ML models, break…