应对深度学习中的质量挑战：MLOps与领域知识的作用
Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledge
AI总结：
本经验论文探讨了MLOps实践与领域知识在解决深度学习系统质量属性（如正确性与资源效率）挑战中的作用，并提供了何时停止系统优化的策略性见解。
摘要：
深度学习（DL）系统在软件工程中面临独特挑战，特别是在正确性和资源效率等质量属性方面。尽管DL模型在特定任务中表现优异，但工程化DL系统依然至关重要。持续改进所需的工作量、成本以及潜在的边际收益递减必须被仔细评估，因为软件工程师常常面临一个关键决策：何时停止相对于质量属性的系统优化。本经验论文探讨了MLOps实践（如监控和实验跟踪）在创建透明且可复现的实验环境中的作用，使团队能够评估并论证设计决策对质量属性的影响。此外，我们报告了通过将领域知识嵌入DL模型设计及其在更大系统中集成来解决质量挑战的经验。研究结果为领域知识和MLOps的益处，以及在DL项目中何时限制进一步优化以最大化整体系统质量和可靠性，提供了可操作的见解。
英文摘要：
Deep learning (DL) systems present unique challenges in software engineering, especially concerning quality attributes like correctness and resource efficiency. While DL models excel in specific tasks, engineering DL systems is still essential. The effort, cost, and potential diminishing returns of continual improvements must be carefully evaluated, as software engineers often face the critical decision of when to stop refining a system relative to its quality attributes. This experience paper explores the role of MLOps practices -- such as monitoring and experiment tracking -- in creating transparent and reproducible experimentation environments that enable teams to assess and justify the impact of design decisions on quality attributes. Furthermore, we report on experiences addressing the quality challenges by embedding domain knowledge into the design of a DL model and its integration within a larger system. The findings offer actionable insights into the benefits of domain knowledge and MLOps and the strategic consideration of when to limit further optimizations in DL projects to maximize overall system quality and reliability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
