Best Practices for Version Control in MLOps and DevOps: Managing Machine Learning Models and Software
Published 16-09-2024
Keywords
- version control,
- MLOps,
- DevOps
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
As the adoption of machine learning (ML) continues to grow in software development environments, effective version control has become essential for managing both machine learning models and traditional software components. This paper investigates best practices for version control within MLOps and DevOps frameworks, focusing on strategies that ensure reproducibility, traceability, and compliance across both domains. We explore the unique challenges posed by ML models, including data dependencies and model drift, and compare them to traditional software versioning practices. Furthermore, we highlight the importance of integrating version control systems with CI/CD pipelines to enable efficient collaboration among multidisciplinary teams. By identifying effective tools, processes, and methodologies, this paper aims to provide practitioners with actionable insights for optimizing version control in their MLOps and DevOps practices.
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