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About this paper
Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl
A comprehensive overview of Machine Learning Operations (MLOps) provides a unifying definition, identifies nine core principles and nine technical components, and outlines an end-to-end architecture to automate ML processes, aiming to increase the success rate of ML models transitioning into production. The work by Kreuzberger, Kühl, and Hirschl (Karlsruhe Institute of Technology and IBM) integrates academic rigor with industry insights.
Key insights
- MLOps is a paradigm and engineering practice situated at the intersection of machine learning, software engineering (DevOps), and data engineering.
- The successful operationalization of ML systems relies on nine core principles, including CI/CD automation, reproducibility, versioning, continuous training and evaluation, and feedback loops.
- Effective MLOps implementation requires a multidisciplinary team and specific technical components that support automated workflows and continuous monitoring.
Summary by alphaXiv.
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