Datasilor

Datasilor is a term used in data management to denote a framework or methodology for tailoring datasets to specific downstream tasks in machine learning and analytics. The word blends data with tailor, signaling a focus on customizing data for particular models, domains, or evaluation scenarios.

In practice, a datasilor workflow includes selecting relevant data, applying transformations, augmenting or curating records, applying privacy-preserving edits, and validating the result against downstream requirements. It places emphasis on reproducibility, provenance, and versioning to enable controlled experiments and fair comparisons.

Architecturally, datasilor often relies on a metadata catalog, a data selector or profiler, modular transform steps, governance and privacy controls, and a pipeline runner that coordinates storage and compute. The design favors plug-ins and adapters to connect with data lakes, warehouses, or feature stores, and may integrate with model monitoring.

Applications include preparing training or evaluation datasets, curating test data with defined risk or bias profiles, and supporting fairness or robustness studies. It is not a formal standard and remains a concept used in industry discussions and some academic work rather than a single technology.

See also data wrangling, data pipeline, data governance, data lineage.