Model Schema

Model schema is a specification of input and output of a model, such as what are the features columns, prediction columns and also ground truth columns. Following are the fields in model schema:

Field	Type	Description	Mandatory
id	int	Unique identifier for each model schema	Not mandatory, if ID is not specified it will create new model schema otherwise it will update the model schema with corresponding ID
model_id	int	Model ID that correlate with the schema	Not mandatory, if not specified the SDK will assign it with the model that user set
spec	InferenceSchema	Detail specification for model schema	True

Detail specification is defined by using InferenceSchema class, following are the fields:

Field	Type	Description	Mandatory
feature_types	Dict[str, ValueType]	Mapping between feature name with the type of the feature	True
model_prediction_output	PredictionOutput	Prediction specification that differ between model types, e.g BinaryClassificationOutput, RegressionOutput, RankingOutput	True
session_id_column	str	The column name that is unique identifier for a request	True
row_id_column	str	The column name that is unique identifier for a row in a request	True
tag_columns	Optional[List[str]]	List of column names that contains additional information about prediction, you can treat it as metadata	False

From above we can see model_prediction_output field that has type PredictionOutput, this field is a specification of prediction that is generated by the model depending on it's model type. Currently we support 3 model types in the schema:

• Binary Classification

• Regression

• Ranking

Each model type has it's own model prediction output specification.

Binary Classification

Model prediction output specification for Binary Classification type is BinaryClassificationOutput that has following fields:

Field	Type	Description	Mandatory
prediction_score_column	str	Column that contains prediction score value of a model. Prediction score must be between 0.0 and 1.0	True
actual_label_column	str	Name of the column containing the actual class	False, because not all model has the ground truth
positive_class_label	str	Label for positive class	True
negative_class_label	str	Label for negative class	True
score_threshold	float	Score threshold for prediction to be considered as positive class	False, if not specified it will use 0.5 as default

Regression

Model prediction output specification for Regression type is RegressionOutput that has following fields:

Field	Type	Description	Mandatory
prediction_score_column	str	Column that contains prediction score value of a model	True
actual_score_column	str	Name of the column containing the actual score	False, because not all model has the ground truth

Ranking

Model prediction output specification for Ranking type is RankingOutput that has following fields:

Field	Type	Description	Mandatory
rank_score_column	str	Name of the column containing the ranking score of the prediction	True
prediction_group_id_column	str	Name of the column containing the prediction group id	True
relevance_score_column	str	Name of the column containing the relevance score of the prediction	True

Define model schema

From the specification above, users can create the schema for their model. Suppose that users have binary classification model, that has 4 features

• featureA that has float type

• featureB that has int type

• featureC that has string type

• featureD that has float type

With positive class complete and negative class non_complete and the threshold for positive class is 0.75. Actual label is stored under column target, prediction_score under column score prediction_id under column prediction_id. From that specification, users can define the model schema and put it alongside version creation. Below is the example snipped code

from merlin.model_schema import ModelSchema
from merlin.observability.inference import InferenceSchema, ValueType, BinaryClassificationOutput
 model_schema = ModelSchema(spec=InferenceSchema(
        feature_types={
            "featureA": ValueType.FLOAT64,
            "featureB": ValueType.INT64,
            "featureC": ValueType.STRING,
            "featureD": ValueType.BOOLEAN
        },
        session_id_column="session_id",
        row_id_column="row_id",
        model_prediction_output=BinaryClassificationOutput(
            prediction_score_column="score",
            actual_label_column="target",
            positive_class_label="complete",
            negative_class_label="non_complete",
            score_threshold=0.75
        )
    ))
with merlin.new_model_version(model_schema=model_schema) as v:
    ....

The above snipped code will define model schema and attach it to certain model version, the reason is the schema for each version is possible to differ.