Example of how to deploy the credit scoring once it passes Giskard's tests.
The deployment happens according to three steps:
- Training: execution of
train.py
script every time apush
occurs. - Testing: execution of
Giskard_test.py
script after the training is done. During this step, the model produced bytrain.py
is uploaded to the Giskard UI (where the user can define his custom test suite). Then all the tests are performed. If at least 50% of the tests pass (this can be tuned here inGiskard_test.py
), the model is tagged as verified. - Deployment: If the model is verified, it is committed to the repo under the folder
deployed_model
that contains:- a subfolder named as the date of the model training which itself contains:
logistic_regression_model.pkl
: a pickle of the verified model.test_data.zip
: a pandas dataframe that was used to verify the model.Giskard_tests.json
: a summary of all the tests and metrics that were run by Giskard.
- a subfolder named as the date of the model training which itself contains: