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 and appropriate handling of missing values. Feature readiness involves well-defined features, encoding schemes, normalization, and the capacity to store and retrieve features in a feature store. Model readiness refers to available artifacts such as pretrained weights or baselines, clearly documented hyperparameters, and versioned model artifacts. Environment readiness ensures consistent software dependencies and hardware requirements, often achieved through containers or managed environments, to enable reproducible runs.

Practices associated with achieving modelready include data profiling and cleaning, dataset versioning, train/validation/test splits, feature engineering, and robust model validation against baselines. It also involves planning for monitoring and governance, with reproducible pipelines and experiment tracking. Automated pipelines and continuous integration/continuous deployment pipelines can enforce gating criteria before a model progresses to production.

Applications and governance: the concept is central to ML operations (MLOps) and enterprise governance, helping teams reduce deployment risk, improve auditability, and support compliance with data handling standards. Modelready artifacts are frequently managed in model registries and lineage systems to track provenance from data sources to deployed models.

Notes: in practice, modelready may be used informally to describe readiness at various stages of the ML lifecycle, from data preparation to deployment, rather than as a formal product.