Build Reliable Machine Learning Pipelines with Continuous Integration

As a data scientist, you are responsible for improving the model currently in production. After spending months fine-tuning the model, you discover one with greater accuracy than the original. Excited by your breakthrough, you create a pull request to merge your model into the main branch. Unfortunately, because of the numerous changes, your team takes over a week to evaluate and analyze them, which ultimately impedes project progress. Furthermore, after deploying the model, you identify unexpected behaviors resulting from code errors, causing the company to lose money.