用MLOps和领域知识提升深度学习系统质量与可靠性
Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledge
- 通过MLOps实现实验可追溯与透明化
- 融合领域知识显著改善模型设计质量
- 指导何时停止优化以平衡系统整体质量
深度学习系统在正确性和资源效率等质量属性上面临独特挑战。尽管模型在特定任务中表现优异，但系统工程仍至关重要。持续改进带来的成本与边际效益递减需谨慎评估，工程师常面临何时停止优化的关键决策。本文通过实践探讨了MLOps（如监控、实验追踪）在构建透明、可复现的实验环境中的作用，使团队能够评估并证明设计决策对质量属性的影响。此外，报告还展示了将领域知识嵌入深度学习模型设计及其系统集成中的经验。研究结果为领域知识与MLOps的价值提供了可操作的洞见，并建议战略性地限制进一步优化，以最大化系统的整体质量与可靠性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。