Software Engineering for Data Scientist — Test-Driven Development

Software Engineering for Data Scientist — Test-Driven Development

Software Engineering for Data Scientist — Test-Driven Development. Our primary focus is to discuss how to expand the horizon of testing in DS/ML projects beyond cross-validation and test data.

Introduction

Test-Driven Development (TDD) is very popular in Software Engineering practice. When it comes to Data Science/Machine Learning projects, it shrinks to validation and cross-validation. In the best-case scenario, we will be talking about A/B testing the model. ML models are becoming part of leger IT system components in the present scenario. This is high time for enterprises to think about TDD in Data Science and Machine Learning projects.

TDD is a software Engineering practice that requires a unit test to be written before the code is supposed to be validated. We are not going to discuss the what and how of test-driven development in Software Engineering. There are plenty of resources available on this topic. Our primary focus is to discuss how to expand the horizon of testing in DS/ML projects beyond cross-validation and test data.

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