Machine learning systems and intelligent applications
Abstract
Machine learning techniques are useful in a wide range of contexts, but techniques alone are insufficient to solve real business problems. We introduce the intelligent applications concept, which characterizes the structure and responsibilities of contemporary machine learning systems.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Benton et al. (2020) proposes an architecture for an ML system that deals with high-volume data, including a component for data federation to deal with structured, unstructured, and streaming data.
“Benton et al. (2020) proposes an architecture for an ML system that deals with high-volume data.This architecture includes a component for data federation to deal with structured, unstructured, and streaming data.”
From A software engineering perspective on engineering machine learning systems: State of the art and challenges (2021) · cites this paper as Benton et al., 2020
Kubernetes ecosystem may ease maintenance and problem localization.
“For instance, Kubernetes ecosystem may ease maintenance and problem localization (Benton, 2020).”
From A software engineering perspective on engineering machine learning systems: State of the art and challenges (2021) · cites this paper as Benton, 2020
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
A survey on concept drift adaptation
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014) · ACM Computing Surveys · doi:10.1145/2523813 · Cited by 3,735 · see citing works
Jupyter Notebooks – a publishing format for reproducible computational workflows
Kluyver, T. A., Ragan-Kelley, B., Fernando, P., Granger, B., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J. B., Grout, J., Corlay, S., et al. (2016) · IOS Press eBooks · doi:10.3233/978-1-61499-649-1-87 · Cited by 3,191 · see citing works
Zaharia, M., Xin, R., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M. J. M., et al. (2016) · Communications of the ACM · doi:10.1145/2934664 · Cited by 2,443 · see citing works
API design for machine learning software: experiences from the scikit-learn projectPreprint
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., et al. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1309.0238 · Cited by 2,387 · see citing works
(2015) · neural information processing systems · Cited by 912 · see citing works
Conway, M. E. (1967) · Cited by 804 · see citing works
Burns, B., Grant, B., Oppenheimer, D., Brewer, E., & Wilkes, J. (2016) · Communications of the ACM · doi:10.1145/2890784 · Cited by 690 · see citing works
Container-based operating system virtualization
Soltesz, S., Pötzl, H., Fiuczynski, M. E., et al. (2007) · doi:10.1145/1272996.1273025 · Cited by 666 · see citing works
Sharing experiments using open‐source software
Nelson, A., Menzies, T., & Gay, G. (2010) · Software Practice and Experience · doi:10.1002/spe.1004 · Cited by 20 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Giray, G. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.111031 · Cited by 187 · see citing works
O'Reilly-Shah, V. N., Gentry, K. R., Walters, A. M., Zivot, J., Anderson, C. T., & Tighe, P. J. (2020) · British Journal of Anaesthesia · doi:10.1016/j.bja.2020.07.040 · Cited by 62 · see citing works
- Authors
- Benton, W. C.
- Affiliations
- Red Hat (United States) (US)
- Corresponding author
- Benton, William C.
- ORCID
- Benton, William C.
- Venue
- IEEE Software
- Published
- 2020-07
- Volume
- 37
- Issue
- 4
- Pages
- 43-49
- ISSN
- 0740-7459 (ISSN-L)
- 1937-4194
- OpenAlex
- W3014985939
- Cited by
- 21
- References
- 10
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 3 · 2022: 6 · 2021: 5 · 2020: 2
- FWCI
- 1.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Software (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- machine learning systems, smart applications, system architecture, business problem solving, software engineering for machine learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
