Published 07-10-2024
Keywords
- DevOps,
- MLOps
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
The integration of DevOps and MLOps practices is transforming the landscape of machine learning (ML) model development, particularly in enhancing feedback loops. Feedback loops are essential for continuous improvement, allowing teams to iterate rapidly, evaluate performance in real-time, and make responsive adjustments to models. This paper explores how the convergence of DevOps and MLOps can optimize these feedback loops, enabling organizations to respond swiftly to changing data environments and user requirements. By examining various strategies, such as automated testing, version control, and continuous deployment, this research highlights the critical role of collaboration between data scientists, IT operations, and other stakeholders. Additionally, the paper presents case studies showcasing successful implementations and outlines best practices for leveraging these methodologies in ML model development. The findings underscore the importance of integrating DevOps and MLOps to foster a culture of continuous improvement, ultimately leading to enhanced model performance and more significant business value.
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