ModelSpec provides a comprehensive specification of how different columns in your
model’s data should be interpreted and used. It categorizes columns into inputs,
outputs, targets, decisions, and metadata, and allows for custom feature definitions
that enhance model monitoring and analysis capabilities.
This specification is crucial for Fiddler to understand your model’s structure,
enabling proper monitoring, drift detection, bias analysis, and explainability
features. It acts as the contract between your model and Fiddler’s monitoring
infrastructure.
- custom_features (List [Multivariate | VectorFeature | TextEmbedding | ImageEmbedding | Enrichment ])
Examples
Creating a basic model spec for classification:
Creating a spec with custom features:
Creating a spec for ranking models:
Attributes
Custom feature definitions
remove_column()
Remove a column name from spec if it exists.