A Preliminary Study of MLOps Practices in GitHub

The rapid and growing popularity of machine learning (ML) applications has led to an increasing interest in MLOps, i.e., the practice of continuous integration and deployment (CI/CD) of ML-enabled systems. Since changes may affect not only the code but also the ML model parameters and the data themselves, the automation of traditional CI/CD needs to be extended to manage model retraining in production.Here we present an initial investigation of the MLOps practices implemented in a set of ML-enabled systems retrieved from GitHub. Our preliminary results suggest that the current adoption of MLOps workflows in open-source GitHub projects is rather limited. Issues are also identified, which can guide future research work.