22 citations · 36 across the 12 of their papers we have counts for
12 papers · 1 filter
Monitoring Machine Learning Systems: A Multivocal Literature Review
Hira Naveed, Scott Barnett, Chetan Arora +3
Context: Dynamic production environments make it challenging to maintain reliable machine learning (ML) systems. Runtime issues, such as changes in data patterns or operating conte…
ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study
Hala Abdelkader, Mohamed Abdelrazek, Scott Barnett +3
Machine learning (ML), especially with the emergence of large language models (LLMs), has significantly transformed various industries. However, the transition from ML model protot…
Seven Failure Points When Engineering a Retrieval Augmented Generation System
Scott Barnett, Stefanus Kurniawan, Srikanth Thudumu +2
Software engineers are increasingly adding semantic search capabilities to applications using a strategy known as Retrieval Augmented Generation (RAG). A RAG system involves findin…
MLGuard: Defend Your Machine Learning Model!
Sheng Wong, Scott Barnett, Jessica Rivera-Villicana +4
Machine Learning (ML) is used in critical highly regulated and high-stakes fields such as finance, medicine, and transportation. The correctness of these ML applications is importa…
Green Runner: A tool for efficient model selection from model repositories
Jai Kannan, Scott Barnett, Anj Simmons +2
Deep learning models have become essential in software engineering, enabling intelligent features like image captioning and document generation. However, their popularity raises co…
Comparative analysis of real bugs in open-source Machine Learning projects -- A Registered Report
Tuan Dung Lai, Anj Simmons, Scott Barnett +2
Background: Machine Learning (ML) systems rely on data to make predictions, the systems have many added components compared to traditional software systems such as the data process…