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Rapid advancements in Machine Learning (ML) introduce unique maintainability and scalability challenges. Our research addresses the evolving challenges and identifies ML maintainability and scalability solutions by conducting a thorough literature review of over 17,000 papers, ultimately refining our focus to 124 relevant sources that meet our stringent selection criteria. We present a catalogue of 41 Maintainability and 13 Scalability challenges and solutions across Data, Model Engineering and the overall development of ML applications and systems. This study equips practitioners with insights on building robust ML applications, laying the groundwork for future research on improving ML system robustness at different workflow stages.
Shivashankar et al. (Thu,) studied this question.