Machine learning model development, characterized by iterative experimentation and adjustments, often leads to complex model iterations, making tracking and debugging challenging. This talk explores the application of CI/CD methodologies to machine learning, using Julia's Pkg ecosystem, Buildkite, GitHub, and MLflow. We showcase a streamlined process for efficient model development and tracking that can lead to mass robust experimentation for machine learning workflows