Automated Testing in Machine Learning Systems

Abstract

As machine learning (ML) systems become integral components of various applications, ensuring their reliability and robustness is paramount. This research explores the domain of automated testing in machine learning systems, addressing the unique challenges associated with testing models trained on complex datasets. The paper reviews existing literature, identifies gaps in current testing methodologies, and presents a comprehensive framework for automated testing in ML.Machine learning plays a pivotal role in numerous applications across various industries, transforming the way we analyze data, make decisions, and solve complex problems. The study delves into different testing techniques, including unit testing, integration testing, and end-to-end testing, tailored specifically for ML models. Challenges such as model interpretability, evolving data distributions, and the need for representative datasets are examined in-depth, providing insights into the intricacies of testing within the ML context.