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A Meta-Summary of Challenges in Building Products with ML Components --
  Collecting Experiences from 4758+ Practitioners

A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners

31 March 2023
Nadia Nahar
Haoran Zhang
Grace A. Lewis
Shurui Zhou
Christian Kastner
ArXivPDFHTML

Papers citing "A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners"

11 / 11 papers shown
Title
Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts
Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts
Jenny T Liang
Melissa Lin
Nikitha Rao
Brad A. Myers
77
5
0
19 Sep 2024
A Large-Scale Study of Model Integration in ML-Enabled Software Systems
A Large-Scale Study of Model Integration in ML-Enabled Software Systems
Yorick Sens
Henriette Knopp
Sven Peldszus
Thorsten Berger
AIFin
31
2
0
12 Aug 2024
Naming the Pain in Machine Learning-Enabled Systems Engineering
Naming the Pain in Machine Learning-Enabled Systems Engineering
Marcos Kalinowski
Daniel Méndez
G. Giray
Antonio Pedro Santos Alves
Kelly Azevedo
...
Stefan Biffl
Jürgen Musil
Michael Felderer
N. Lavesson
T. Gorschek
24
5
0
20 May 2024
How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy
  Efficiency Tradeoffs in Concept Drift Detection
How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detection
Rafiullah Omar
Justus Bogner
J. Leest
Vincenzo Stoico
Patricia Lago
H. Muccini
17
1
0
30 Apr 2024
Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using
  LLMs
Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using LLMs
Chenyang Yang
Rishabh Rustogi
Rachel A. Brower-Sinning
Grace A. Lewis
Christian Kastner
Tongshuang Wu
KELM
35
12
0
14 Oct 2023
From plane crashes to algorithmic harm: applicability of safety
  engineering frameworks for responsible ML
From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML
Shalaleh Rismani
Renee Shelby
A. Smart
Edgar W. Jatho
Joshua A. Kroll
AJung Moon
Negar Rostamzadeh
42
36
0
06 Oct 2022
Operationalizing Machine Learning: An Interview Study
Operationalizing Machine Learning: An Interview Study
Shreya Shankar
Rolando Garcia
J. M. Hellerstein
Aditya G. Parameswaran
71
51
0
16 Sep 2022
Towards Guidelines for Assessing Qualities of Machine Learning Systems
Towards Guidelines for Assessing Qualities of Machine Learning Systems
Julien Siebert
Lisa Joeckel
J. Heidrich
K. Nakamichi
Kyoko Ohashi
I. Namba
Rieko Yamamoto
M. Aoyama
28
47
0
25 Aug 2020
Trust in Data Science: Collaboration, Translation, and Accountability in
  Corporate Data Science Projects
Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects
Samir Passi
S. Jackson
171
108
0
09 Feb 2020
Human-AI Collaboration in Data Science: Exploring Data Scientists'
  Perceptions of Automated AI
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
Dakuo Wang
Justin D. Weisz
Michael J. Muller
Parikshit Ram
Werner Geyer
Casey Dugan
Y. Tausczik
Horst Samulowitz
Alexander G. Gray
178
308
0
05 Sep 2019
Improving fairness in machine learning systems: What do industry
  practitioners need?
Improving fairness in machine learning systems: What do industry practitioners need?
Kenneth Holstein
Jennifer Wortman Vaughan
Hal Daumé
Miroslav Dudík
Hanna M. Wallach
FaML
HAI
192
743
0
13 Dec 2018
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