Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledge
Deep learning (DL) systems present unique challenges in software engineering, especially concerning quality attributes like correctness and resource efficiency. While DL models achieve exceptional performance in specific tasks, engineering DL-based 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 not only the benefits of domain knowledge and MLOps but also the strategic consideration of when to limit further optimizations in DL projects to maximize overall system quality and reliability
Mon 28 AprDisplayed time zone: Eastern Time (US & Canada) change
14:00 - 15:30 | Quality Assurance for AI systemsResearch and Experience Papers at 208 Chair(s): Eduardo Santana de Almeida Federal University of Bahia | ||
14:00 10mTalk | Towards a Domain-Specific Modeling Language for Streamlined Change Management in AI Systems Development Research and Experience Papers Razan Abualsaud IRIT, CNRS, Toulouse | ||
14:10 15mTalk | An AI-driven Requirements Engineering Framework Tailored for Evaluating AI-Based Software Research and Experience Papers Hamed Barzamini , Fatemeh Nazaritiji Northern Illinois University, Annalise Brockmann Northern Illinois University, Hasan Ferdowsi Northern Illinois university, Mona Rahimi Northern Illinois University | ||
14:25 15mTalk | MLScent: A tool for Anti-pattern detection in ML projects Research and Experience Papers | ||
14:40 15mTalk | Debugging and Runtime Analysis of Neural Networks with VLMs (A Case Study)Distinguished paper Award Candidate Research and Experience Papers Boyue Caroline Hu University of Toronto, Divya Gopinath KBR; NASA Ames, Ravi Mangal Colorado State University, Nina Narodytska VMware Research, Corina S. Păsăreanu Carnegie Mellon University, Susmit Jha SRI | ||
14:55 15mTalk | Investigating Issues that Lead to Code Technical Debt in Machine Learning Systems Research and Experience Papers Rodrigo Ximenes Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Antonio Pedro Santos Alves Pontifical Catholic University of Rio de Janeiro, Tatiana Escovedo Pontifical Catholic University of Rio de Janeiro, Rodrigo Spinola Virginia Commonwealth University, Marcos Kalinowski Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Pre-print | ||
15:10 10mTalk | Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledge Research and Experience Papers Santiago del Rey Universitat Politècnica De Catalunya - Barcelona Tech, Adrià Medina Universitat Politècnica de Barcelona - BarcelonaTech (UPC), Xavier Franch Universitat Politècnica de Catalunya, Silverio Martínez-Fernández UPC-BarcelonaTech Pre-print | ||
15:20 10mOther | Discussion Research and Experience Papers |