ModelMeta
ModelMeta is a framework designed for managing and versioning machine learning models. It provides a structured approach to track the entire lifecycle of a model, from its initial development and training to its deployment and ongoing monitoring. The core idea behind ModelMeta is to treat machine learning models as first-class citizens, allowing for robust reproducibility, auditability, and collaboration within data science teams.
The framework typically involves storing metadata associated with each model, such as training data details, hyperparameters
Key features often found in ModelMeta implementations include experiment tracking, model registry, and lineage tracking. Experiment