Aprenda Testing ML Models

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Automated testing is a cornerstone of robust machine learning (ML) deployment pipelines. By systematically verifying each component of your ML workflow, you can catch errors early, prevent regressions, and ensure that your models perform reliably in production. Testing strategies for ML models typically include both unit tests and integration tests.

Unit tests focus on small, isolated pieces of code—such as data preprocessing functions or feature engineering steps—ensuring that each performs as expected;
Integration tests, on the other hand, validate that multiple components work together correctly, such as checking that a trained model produces inference outputs with the correct shape and type when given new data.


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import unittest
import numpy as np
from sklearn.linear_model import LogisticRegression

class TestModelPredictionShape(unittest.TestCase):
    def test_prediction_output_shape(self):
        # Simulate training data
        X_train = np.random.rand(10, 3)
        y_train = np.random.randint(0, 2, 10)
        # Train a simple model
        model = LogisticRegression()
        model.fit(X_train, y_train)
        # Simulate new input data
        X_new = np.random.rand(5, 3)
        # Get predictions
        predictions = model.predict(X_new)
        # Assert that output shape matches expected
        self.assertEqual(predictions.shape, (5,))

if __name__ == "__main__":
    unittest.main(argv=[''], exit=False)

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