Curation & Quality

The Curation & Quality sub-category focuses on the "Garbage In, Garbage Out" problem of AI. This section is dedicated to the rigorous engineering required to clean, label, and de-bias the massive datasets used in model training. We discuss human-in-the-loop workflows, automated data cleaning pipelines, and the ethical considerations of data sourcing. This is the place for data engineers who want to share strategies for improving model performance through high-fidelity data engineering rather than just increasing parameter counts.