Maintainability Challenge

Maintaining machine learning (ML) systems presents unique challenges beyond traditional software, stemming from the interconnectedness of data, models, and engineering workflows. Current research focuses on identifying and mitigating these challenges across the ML lifecycle, employing techniques like systematic literature reviews to catalog issues and leveraging large language models (LLMs) to assess and improve code maintainability, often focusing on metrics like complexity and readability. These efforts aim to reduce the high costs and risks associated with updating and adapting ML systems, ultimately improving the reliability and longevity of AI-driven applications.