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Informative Article | Science and Technology | India | Volume 10 Issue 4, April 2021 | Popularity: 4.8 / 10
Navigating Technical Debt in the Evolving Landscape of Machine Learning and Artificial Intelligence
Sowmya Ramesh Kumar
Abstract: This paper illuminates the profound impact of Machine Learning (ML) and Artificial Intelligence (AI) in modern industries, charting their journey from academic concepts to vital business tools. The surge in implementation, however, introduces a critical challenge: technical debt. Expanding on three key dimensions, code dependencies, data dependencies, and system dependencies—the paper explores the entanglement challenge in code, untangling complexity in data dependencies, and addressing high-debt design patterns in systems. It advocates for a holistic approach to measuring and mitigating technical debt, emphasizing guiding questions and good practices. The article concludes by stressing the imperative of fostering a culture that recognizes and prioritizes the reduction of technical debt for sustained success in the dynamic landscape of ML and AI.
Keywords: Machine learning, artificial intelligence, refactoring, hidden feedback loops, technical debt, artificial intelligence, coding, production
Edition: Volume 10 Issue 4, April 2021
Pages: 1370 - 1371
DOI: https://www.doi.org/10.21275/SR24212235641
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