Continuous Testing in DevOps and MLOps
Establishing Robust Validation for Machine Learning Models
Keywords:
Continuous Testing, DevOps, MLOps, Machine Learning, Software Development LifecycleAbstract
In the era of rapid software delivery, the integration of continuous testing in DevOps and MLOps has emerged as a critical component for ensuring the reliability and effectiveness of machine learning models throughout their lifecycle. This paper investigates how continuous testing can be embedded within DevOps and MLOps pipelines to validate machine learning models not only during development but also after deployment. By establishing robust validation mechanisms, organizations can minimize risks associated with model performance degradation and enhance overall system reliability. The study emphasizes the importance of automated testing strategies, including unit tests, integration tests, and performance tests, tailored specifically for machine learning applications. Furthermore, it discusses the challenges faced in implementing continuous testing in these environments and offers practical recommendations to overcome them. Ultimately, this research aims to provide a comprehensive understanding of continuous testing's role in enhancing the quality of machine learning models in DevOps and MLOps contexts.
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