modelready
Modelready is a term used in data science and machine learning to describe the state of data, models, and supporting infrastructure that are prepared for training, evaluation, or deployment. It encompasses data quality, feature availability, reproducibility, and operational readiness necessary to build reliable models.
Typically, modelready includes several components. Data readiness covers clean, labeled, and consistent data with documented lineage
Practices associated with achieving modelready include data profiling and cleaning, dataset versioning, train/validation/test splits, feature engineering,
Applications and governance: the concept is central to ML operations (MLOps) and enterprise governance, helping teams
Notes: in practice, modelready may be used informally to describe readiness at various stages of the ML