The speed and reliability of CI/CD pipelines directly impact developer velocity and release quality. However, deployment durations can vary widely due to factors like code complexity, pipeline structure, testing strategies, and environment configurations. This article explores how to build a machine learning regression model that predicts deployment time based on features derived from CI/CD metadata, code metrics, and infrastructure events.
Why Predict Deployment Duration?
Predicting deployment time can:
