  Background. The rapid and growing popularity of machine learning (ML)
applications has led to an increasing interest in MLOps, that is, the practice
of continuous integration and deployment (CI/CD) of ML-enabled systems. Aims.
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. Method. In this paper, we present an
initial investigation of the MLOps practices implemented in a set of ML-enabled
systems retrieved from GitHub, focusing on GitHub Actions and CML, two
solutions to automate the development workflow. Results. Our preliminary
results suggest that the adoption of MLOps workflows in open-source GitHub
projects is currently rather limited. Conclusions. Issues are also identified,
which can guide future research work.

Software Engineering;Machine Learning