How Does CI/CD Differ for Machine Learning Pipelines (MLOps)?

Engineering teams often find that traditional CI/CD pipelines, which are designed for deterministic software delivery, face challenges when applied to machine learning (ML) systems due to the non-deterministic nature of ML outputs, the need for data versioning, and the continuous retraining of models. Unlike conventional software, ML systems treat data as a first-class dependency, resulting in probabilistic outputs that can degrade over time due to data drift. This necessitates rethinking pipeline design, as traditional CI/CD approaches are not equipped to handle the complexities of MLOps. Key differences include the need to version datasets, feature engineering logic, and model artifacts, as well as testing for behavior rather than just logic, such as accuracy and model drift. Additionally, ML pipelines are iterative and branching, focusing on continuous retraining and monitoring rather than static releases. Platforms like Semaphore can help orchestrate these complex workflows by offering flexible pipeline orchestration, cost efficiency, and reliable performance at scale.