Validating your code, data, and models

When implementing a CI/CD pipeline, you need to make sure you have all the necessary tests in place to deploy your newly created code with ease and confidence. Once you are running a CI or a CI/CD pipeline, the power of automated tests will become immediately evident. It not only protects certain pieces of code from failing while you are developing them, but it also protects your entire process—including the environment, data requirements, model initialization, optimization, resource requirements, and deployment—for the future.

When implementing a validation pipeline for our ML process, we align ourselves with the classical application development principles:

• Unit testing
• Integration testing
• End-to-end testing

We can translate these testing techniques directly to input data, models, and the application code of the scoring service.

Rethinking unit testing for data quality

Unit tests are essential to...