A Preliminary Investigation of MLOps Practices in GitHub

• URL: http://arxiv.org/abs/2209.11453v1
• Date: Fri, 23 Sep 2022 07:29:56 GMT
• Title: A Preliminary Investigation of MLOps Practices in GitHub
• Authors: Fabio Calefato, Filippo Lanubile, Luigi Quaranta
• Abstract summary: Machine learning applications have led to an increasing interest in MLOps. We present an initial investigation of the MLOps practices implemented in a set of ML-enabled systems retrieved from GitHub.
• Score: 10.190501703364234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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