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Hidden Technical Debt in Machine Learning Systems
unpublished
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Using the software engineering framework of technical debt, we find it is common to incur massive ongoing maintenance costs in real-world ML systems. ...
Machine learning offers a fantastically powerful toolkit for building useful complex prediction systems quickly. This paper argues it is dangerous to think of these quick wins as coming for free. ...
Acknowledgments This paper owes much to the important lessons learned day to day in a culture that values both innovative ML research and strong engineering practice. ...
fatcat:7sbcfequrba6hjxyqlsauigoxu
Quality Assurance Challenges for Machine Learning Software Applications During Software Development Life Cycle Phases
[article]
2021
arXiv
pre-print
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In the past decades, the revolutionary advances of Machine Learning (ML) have shown a rapid adoption of ML models into software systems of diverse types. ...
Such Machine Learning Software Applications (MLSAs) are gaining importance in our daily lives. As such, the Quality Assurance (QA) of MLSAs is of paramount importance. ...
The latest trend is the widespread interest in the adoption of ML (Machine Learning) capabilities into large scale Machine Learning software applications (MLSAs) [1] . ...
arXiv:2105.01195v2
fatcat:ksvuixfvxjeddpky4ifmnnh5re
Technology Readiness Levels for AI ML
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2006.12497v2 fatcat:h6qx3y7575cefbgefowlwpx5paOther Versions
Drawing on experience in both spacecraft engineering and AI/ML (from research through product), we propose a proven systems engineering approach for machine learning development and deployment. ...
The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. ...
We've introduced TRL4ML, a proven systems engineering process for machine learning. ...
arXiv:2006.12497v3
fatcat:nn2co36ypbewvlpfrqu7vuc3gq
Design of a Financial Decision Support System based on Artificial Neural Networks for Stock Price Prediction
2019
JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES
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Machine learning is widely being used in the financial domain including prediction of stock prices. ...
Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. ...
parameters which will enable the system to comprehensively predict stock prices.The authors are working on enhancing the machine learning models and fine tuning the financial parameters to more accurately ...
doi:10.26782/jmcms.2019.10.00060
fatcat:h5u2jom7ajbdhh3st3h7shvsg4
A Debt-Aware Learning Approach for Resource Adaptations in Cloud Elasticity Management
[article]
2017
arXiv
pre-print
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To address this limitation, we propose a technical debt-aware learning approach for autonomous elasticity management based on a reinforcement learning of elasticity debts in resource provisioning; the ...
The evaluation shows that a reinforcement learning of technical debts in elasticity obtains a higher utility for a cloud customer, while conforming expected levels of performance. ...
We instantiated from the reference system model in figure 2 a scenario with a single debt-aware learning agent.
A. ...
arXiv:1702.07431v1
fatcat:d3fbejh2qzf3hmm2buwdc2shdm
Cognitive Computing Architectures for Machine (Deep) Learning at Scale
2017
Proceedings (MDPI)
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The paper reviews existing models for organizing information for machine learning systems in heterogeneous computing environments. ...
We extend these concepts to the design of Cognitive Distributed Learning Systems to resolve critical bottlenecks in real-time machine learning applications such as Predictive Analytics and Recommender ...
Figure 4 . 4 Computational requirements for scale-out learning (from [5] ).
Figure 5 . 5 The hidden technical debt in machine learning systems (adapted from [6] ). ...
doi:10.3390/is4si-2017-04025
fatcat:3ieob5bxwnd5hgjb7mytoxyszi
Making Contextual Decisions with Low Technical Debt
[article]
2017
arXiv
pre-print
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Reinforcement-based learning algorithms such as contextual bandits can be very effective in these settings, but applying them in practice is fraught with technical debt, and no general system exists that ...
The service makes real-time decisions and learns continuously and scalably, while significantly lowering technical debt. ...
Systems for ML and experimentation We previously discussed these systems with regards to technical debt ( §2). A more general discussion follows. A/B testing. ...
arXiv:1606.03966v2
fatcat:6aafbkrqaregbhykmw525o4lqq
What do you mean? Research in the Age of Machines
2019
College & research libraries news
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was an undeniable bop of its era in which Justin Bieber explores the ambiguities of romantic communication. ...
(I pinky promise this will soon make sense for scholarly communication librarians interested in artificial intelligence [AI].) ...
Data mining and machine-learning are a great boon for political campaigns and corporate marketing wings that thrive on the ability to uncover hidden connections in consumer behavior, in order to influence ...
doi:10.5860/crln.80.10.565
fatcat:2l3lh3yaavck3fzid6uzzrbluq
Share Price Prediction of Aerospace Relevant Companies with Recurrent Neural Networks based on PCA
[article]
2020
arXiv
pre-print
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The developed model could be an intelligent agent in an automatic stock prediction system, with which, the financial industry could make a prompt decision for their economic strategies and business activities ...
The capital market plays a vital role in marketing operations for aerospace industry. ...
Moreover, various machine-learning techniques have been widely used in the finance industry. ...
arXiv:2008.11788v1
fatcat:p4euefg5e5gv5dbriy4afo635a
A Framework for Conditional Statement Technical Debt Identification and Description
[article]
2022
arXiv
pre-print
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However, there are many cases where technical debt instances are not explicitly acknowledged but deeply hidden in the code. ...
While our approach is applicable in principle to any type of code fragments, we focus in this study on technical debt hidden in conditional statements, one of the most TD-carrying parts of code. ...
This investigation ensured that the SATD comments represent technical debt in source code. ...
arXiv:2012.12466v2
fatcat:vykl4yb6kfaefoiikpo3mhh3qy
Prevalence, Contents and Automatic Detection of KL-SATD
[article]
2020
arXiv
pre-print
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When developers use different keywords such as TODO and FIXME in source code comments to describe self-admitted technical debt (SATD), we refer it as Keyword-Labeled SATD (KL-SATD). ...
Our machine learning classifier using logistic Lasso regression has good performance in detecting KL-SATD comments (AUC-ROC 0.88). ...
Selfadmitted technical debt (SATD) refers to a specific type of code debt, where the developer acknowledges admitting code debt into the system [2] . ...
arXiv:2008.05159v1
fatcat:mh5kzlopsbc7zajschcn25zl5e
Data Smells in Public Datasets
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2203.08007v1 fatcat:ny2g7o56vvalphltrsp7zjk35eOther Versions
Analogous to code smells, we introduce a novel catalogue of data smells that can be used to indicate early signs of problems or technical debt in machine learning systems. ...
The adoption of Artificial Intelligence (AI) in high-stakes domains such as healthcare, wildlife preservation, autonomous driving and criminal justice system calls for a data-centric approach to AI. ...
Due to their highly tangled and experimental nature, machine learning systems are prone to rapid accumulation of technical debt [4, 5, 8, 46] . ...
arXiv:2203.08007v2
fatcat:5mhual47krfflg5bjsu3wefxle
THE EVOLUTION OF KDD: TOWARDS DOMAIN-DRIVEN DATA MINING
2007
International journal of pattern recognition and artificial intelligence
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Key components of domain-driven data mining are constrained context, integrating domain intelligence, human-machine cooperation, in-depth mining, actionability enhancement, and iterative refinement process ...
Its typical task is to let data tell a story disclosing hidden information regarding a business issue. ...
Data used in this paper is from Australian Centrelink and Capital Market CRC, AC3 and SIRCA. ...
doi:10.1142/s0218001407005612
fatcat:6ln5hcaxefaftp44kjdywbpjy4
Debt-Prone Bugs: Technical Debt in Software Maintenance
[article]
2017
arXiv
pre-print
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In this article, we propose the concept of debt-prone bugs to model the technical debt in software maintenance. ...
Such confliction leads to a trade-off between software quality and release schedule, which is known as the technical debt metaphor. ...
[1] summarize the state of art in managing technical debt in software systems and present the potential future work. In bug tracking systems, Storey et al. [9] , Wang et al. [10] , and Guo et al. ...
arXiv:1704.04766v1
fatcat:ezkgllmpnvc7xidncd22g5ypc4
Debt-Prone Bugs: Technical Debt in Software Maintenance
2012
International Journal of Advancements in Computing Technology
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In this article, we propose the concept of debt-prone bugs to model the technical debt in software maintenance. ...
Such confliction leads to a trade-off between software quality and release schedule, which is known as the technical debt metaphor. ...
[1] summarize the state of art in managing technical debt in software systems and present the potential future work. In bug tracking systems, Storey et al. [9] , Wang et al. [10] , and Guo et al. ...
doi:10.4156/ijact.vol4.issue19.54
fatcat:gtp3rbrkincdlp36chrq7iurxm
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