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titleinput_schema.avsc
Code Block
languagejson
{
    "type": "array",
    "items": {
        "type": "record",
        "name": "inferred_schema",
        "fields": [
            {
                "name": "UUID",
                "type": "string"
            },
            {
                "name": "amount",
                "type": ["int", "double"]
            },
            {
                "name": "home_ownership",
                "type": "string"
            },
            {
                "name": "age",
                "type": "string"
            },
            {
                "name": "credit_age",
                "type": ["int", "null"]
            },
            {
                "name": ["intemployed",
    "double"]            "type": "boolean"
            },
            {
                "name": "home_ownershiplabel",
                "type": "stringint"
            },
            {
                "name": "ageprediction",
                "type": "stringint"
            }
        ]
    }
}

Type Unions & Missing Values

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If a field is allowed to have missing values, such as credit_age in the example above, the Avro schema can accommodate this through a type union that includes "null" along with the base type, such as "type": ["nullint", "intnull"].

Out-of-Bounds Values

The Avro specification does not allow for checking the bounds of numerical fields. As such, If a value is out-of-bounds, it is recommended to put a check in the model source code which changes the type of that field. For instance, if a model is supposed to output a probability, then in the (Python) source code one could include the line

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