smoke-test-ml-pipeline

What problem does it solve?

This Skill automates the smoke testing of Machine Learning pipelines, ensuring the integrity of the predictive models before deployment.

Core Features & Use Cases

• Smoke Testing: Automatically verifies the structure and performance of machine learning models by running a minimal pytest.
• Diagnostic by Construction: Identifies issues with data handling and model predictions early in the development cycle.
• Use Case: Prior to deploying a new machine learning model, this Skill can be used to verify that the model behaves as expected and meets the necessary performance criteria.

Quick Start

Execute the smoke test by running pytest tests/smoke/test_NN_<short_name>.py to verify the structure and performance of the model.