no code implementations • 14 Oct 2023 • Chenyang Yang, Rishabh Rustogi, Rachel Brower-Sinning, Grace A. Lewis, Christian Kästner, Tongshuang Wu
Current model testing work has mostly focused on creating test cases.
1 code implementation • 3 Mar 2023 • Katherine R. Maffey, Kyle Dotterrer, Jennifer Niemann, Iain Cruickshank, Grace A. Lewis, Christian Kästner
Many organizations seek to ensure that machine learning (ML) and artificial intelligence (AI) systems work as intended in production but currently do not have a cohesive methodology in place to do so.
no code implementations • 11 Nov 2022 • Chenyang Yang, Rachel Brower-Sinning, Grace A. Lewis, Christian Kästner, Tongshuang Wu
In spite of machine learning's rapid growth, its engineering support is scattered in many forms, and tends to favor certain engineering stages, stakeholders, and evaluation preferences.
no code implementations • 25 Mar 2021 • Grace A. Lewis, Stephany Bellomo, Ipek Ozkaya
However, end-to-end development of ML-enabled systems, as well as their seamless deployment and operations, remain a challenge.
no code implementations • 14 Oct 2019 • Grace A. Lewis, Stephany Bellomo, April Galyardt
As a result, assumptions and even descriptive language used by practitioners from these different disciplines can exacerbate other challenges to integrating ML/AI components into larger systems.