Specifying Model Signatures¶

To ensure a consistent experience no matter where a model is run, the Snowflake Model Registry needs to know the input and output schema of the model’s inference methods: that is, the name and type of all columns in the input or output DataFrame. This allows these columns to be mapped between Python and SQL data types when necessary. This schema is referred to as a signature by analogy to the arguments of a function and their types.

For certain ML frameworks, the model registry can infer these schemas, either from data structures in the model itself or from sample input data. However, often, models accept or return objects that lack this information, such as NumPy arrays. In these cases, Snowpark ML infers the input feature names as input_feature_1, input_feature_2, and so on. Similarly, output features are named output_feature_1, output_feature_2, etc.

To use more meaningful names in your custom models, you can do one of the following:

• Update sample_input_data with column names, usually by converting it to a pandas or Snowpark DataFrame.

• Explicitly pass signatures to log_model. When a model does not produce names in its output, explicit signatures might be the only option.

import pandas as pd
from sklearn import svm, datasets

from snowflake.ml.model import model_signature

digits = datasets.load_digits()
target_digit = 6

def one_vs_all(dataset, digit):
    return [x == digit for x in dataset]

train_features = digits.data[:10]
train_labels = one_vs_all(digits.target[:10], target_digit)
clf = svm.SVC(gamma=0.001, C=10.0, probability=True)
clf.fit(train_features, train_labels)

sig = model_signature.infer_signature(
    train_features,
    labels_df,
    input_feature_names=['column1', 'column2', ...],
    output_feature_names=['is_target_digit'])

# Supply signature for every functions the model exposes. In this case only `predict`.
mv = reg.log_model(clf, model_name='my_model', version_name='v1', signatures={"predict": sig})

sig = ModelSignature(
    inputs=[
        FeatureSpec(type=DataType.DOUBLE, name=f_0),
        FeatureSpec(type=DataType.INT64,
                name=sparse_0_fixed_len, shape=(5, 5)),
                FeatureSpec(type=DataType.INT64, name=sparse_1_variable_len, shape=(-1,)),
    ],
    outputs=[
        FeatureSpec(type=DataType.FLOAT, name=output),
    ]
)