Welcome to this tutorial repository, where we explore the use of Giskard in the context of CI/CD integration.

This tutorial is part of the Mastering Machine Learning Validation with Giskard: From Detection to CI/CD Integration article.

To see how Giskard detects vulnerabilities in our model in action, you can demo the notebook in Google Colab:

• Model Validation: Detect potential issues in your machine learning models using Giskard's comprehensive scanning capabilities.
• Test Suites: Create and customize test suites to systematically validate your models and ensure issues are addressed.
• Custom Tests: Tailor your validation with custom tests to meet specific requirements.
• CI/CD Integration: Integrate Giskard into your CI/CD pipeline to ensure your models are always validated before deployment.

Below, we'll explain the code structure and how to use it effectively.

We have provided a simple Python script named run_test_suites.py that utilizes the giskard package to scan a model, run a test suite, and save the results in a JSON format, suitable for consumption by your CI/CD pipeline.

The script is designed to be triggered by your CI/CD pipeline when a pull request is created or updated. The pipeline will execute the script and display the results in the pull request comments.

In this tutorial, we use a simple logistic regression model trained on the Customer Churn dataset.

You can find the trained model in the model directory, and the validation dataset is provided in the data directory.

Snapshots of the Scan Results, Test Suite Results, and CI/CD Integration are provided below.