ModelOps: Cloud-Based Lifecycle Management for Reliable and Trusted AI

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Han, S., Mao, H., & Dally, W. J. (2015) · arXiv · doi:10.48550/arxiv.1510.00149 · Cited by 4,662 · see citing works

Untitled work

(2015) · neural information processing systems · Cited by 1,305 · see citing works

Untitled work

(2015) · neural information processing systems · Cited by 912 · see citing works

Explanations based on the Missing: Towards Contrastive Explanations with Pertinent NegativesPreprint

Dhurandhar, A., Chen, P.-Y., Luss, R., Tu, C.-C., Ting, P.-S., Shanmugam, K., & Das, P. (2018) · Neural Information Processing Systems · doi:10.48550/arxiv.1802.07623 · Cited by 730 · see citing works

Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective

Hazelwood, K., Bird, S., Brooks, D., Chintala, S., Diril, U., Dzhulgakov, D., Fawzy, M., Jia, B., Jia, Y., Kalro, A., et al. (2018) · 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA) · doi:10.1109/hpca.2018.00059 · Cited by 590 · see citing works

Devops: A Software Architect's Perspective

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Continuous Integration: Improving Software Quality and Reducing Risk

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FactSheets: Increasing trust in AI services through supplier's declarations of conformity

Arnold, M., Bellamy, R. K. E., Hind, M., Houde, S., Mehta, S., Mojsilović, A., Nair, R., Ramamurthy, K. N., Olteanu, A., Piorkowski, D., et al. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2942288 · Cited by 434 · see citing works

TFX: A TensorFlow-Based Production-Scale Machine Learning Platform

Baylor, D., Breck, E., Cheng, H.-T., Fiedel, N., Foo, C. Y., Haque, Z., Haykal, S., Ispir, M., Jain, V., Koc, L., et al. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098021 · Cited by 350 · see citing works

Efficient Defenses Against Adversarial Attacks

Zantedeschi, V., Nicolae, M.-I., & Rawat, A. (2017) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/3128572.3140449 · Cited by 328 · see citing works

Evaluating the Robustness of Neural Networks: An Extreme Value Theory ApproachPreprint

Weng, T.-W., Zhang, H., Chen, P., Yi, J., Su, D., Gao, Y., Hsieh, C., & Daniel, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.10578 · Cited by 307 · see citing works

Data Management Challenges in Production Machine Learning

Polyzotis, N., Roy, S., Whang, S. E., & Zinkevich, M. (2017) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3035918.3054782 · Cited by 214 · see citing works

Container and Microservice Driven Design for Cloud Infrastructure DevOps

Kang, H., Le, M. V., & Tao, S. (2016) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e.2016.26 · Cited by 179 · see citing works

M odel DB

Vartak, M., Subramanyam, H., Lee, W.-E., Viswanathan, S., Husnoo, S., Madden, S. R., & Zaharia, M. (2016) · Workshop on Human-In-the-Loop Data Analytics (HILDA) · doi:10.1145/2939502.2939516 · Cited by 157 · see citing works

Towards Unified Data and Lifecycle Management for Deep Learning

Miao, H., Li, A., Davis, L. S., & Deshpande, A. (2017) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2017.112 · Cited by 104 · see citing works

IBM Deep Learning Service

Bhattacharjee, B., Boag, S., Doshi, C., Dube, P., Herta, B., Ishakian, V., Jayaram, K. R., Khalaf, R. Y., Krishna, A., Li, Y. B., et al. (2017) · IBM Journal of Research and Development · doi:10.1147/jrd.2017.2716578 · Cited by 46 · see citing works

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(2018) · arXiv (Cornell University) · Cited by 41 · see citing works

Scaling Machine Learning as a Service.

Li, L. E., Chen, E., Hermann, J., Zhang, P., & Wang, L. (2016) · Cited by 23 · see citing works

CRM Sales Prediction Using Continuous Time-Evolving Classification

Ali, M., & Lee, Y. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11418 · Cited by 10 · see citing works

An End-To-End Machine Learning Pipeline That Ensures Fairness PoliciesPreprint

Shaikh, S., Vishwakarma, H., Mehta, S., Varshney, K. R., Ramamurthy, K. N., & Wei, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.06876 · Cited by 9 · see citing works

Hardening Deep Neural Networks via Adversarial Model Cascades

Vijaykeerthy, D., Suri, A., Mehta, S., & Kumaraguru, P. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851970 · Cited by 8 · see citing works

Automatic Dependency Management for Scientific Applications on Clusters

Tovar, B., Hazekamp, N., Kremer-Herman, N., & Thain, D. (2018) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e.2018.00026 · Cited by 5 · see citing works

Horizontal Scaling With a Framework for Providing AI Solutions Within a Game Company

Kolen, J. F., Sardari, M., Mattar, M., Peterson, N., & Wu, M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11388 · Cited by 4 · see citing works

Ready or not, AI comes—an interview study of organizational AI readiness factors

Jöhnk, J., Weißert, M., & Wyrtki, K. (2021) · Business & Information Systems Engineering · doi:10.1007/s12599-020-00676-7 · Cited by 637 · see citing works

Trustworthy AI: From Principles to Practices

Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612 · see citing works

Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, applications, challenges and future research directions

Habbal, A., Ali, M. K., & Abuzaraida, M. A. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122442 · Cited by 407 · see citing works

Software Engineering for AI-Based Systems: A Survey

Martínez‐Fernández, S., Bogner, J., Franch, X., Oriol, M., Siebert, J., Trendowicz, A., Vollmer, A. M., & Wagner, S. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3487043 · Cited by 374 · see citing works

A software engineering perspective on engineering machine learning systems: State of the art and challenges

Giray, G. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.111031 · Cited by 187 · see citing works

Optimized container scheduling for data-intensive serverless edge computing

Rausch, T., Rashed, A., & Dustdar, S. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.07.017 · Cited by 190 · see citing works

Adoption and Effects of Software Engineering Best Practices in Machine Learning

Serban, A., van der Blom, K., Hoos, H., & Visser, J. (2020) · Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) · doi:10.1145/3382494.3410681 · Cited by 127 · see citing works

The pipeline for the continuous development of artificial intelligence models—Current state of research and practice

Steidl, M., Felderer, M., & Ramler, R. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111615 · Cited by 126 · see citing works

A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle

Shahriar, S., Allana, S., Hazratifard, S. M., & Dara, R. (2023) · IEEE Access · doi:10.1109/access.2023.3287195 · Cited by 114 · see citing works

Shifting ML value creation mechanisms: A process model of ML value creation

Shollo, A., Hopf, K., Thiess, T., & Müller, O. (2022) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2022.101734 · Cited by 96 · see citing works

Towards MLOps: A Case Study of ML Pipeline Platform

Zhou, Y., Yu, Y., & Ding, B. (2020) · 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice51518.2020.00102 · Cited by 103 · see citing works

The art and practice of data science pipelines

Biswas, S., Wardat, M., & Rajan, H. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3510003.3510057 · Cited by 83 · see citing works

A Joint Study of the Challenges, Opportunities, and Roadmap of MLOps and AIOps: A Systematic Survey

Díaz-de-Arcaya, J., Torre-Bastida, A. I., Zárate, G., Miñón, R., & Almeida, A. (2023) · ACM Computing Surveys · doi:10.1145/3625289 · Cited by 105 · see citing works

Edge Intelligence: The Convergence of Humans, Things, and AI

Rausch, T., & Dustdar, S. (2019) · 2019 IEEE International Conference on Cloud Engineering (IC2E) · doi:10.1109/ic2e.2019.00022 · Cited by 82 · see citing works

Managing artificial intelligence applications in healthcare: Promoting information processing among stakeholders

Lämmermann, L., Hofmann, P., & Urbach, N. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102728 · Cited by 67 · see citing works

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

Lekadir, K., Osuala, R., Gallin, C., Lazrak, N., Kushibar, K., Tsakou, G., Aussó, S., Alberich, L. C., Marias, K., Tsiknakis, M., et al. (2021) · arXiv · doi:10.48550/arxiv.2109.09658 · Cited by 58 · see citing works

Machine Learning Orchestration in Cloud Environments: Automating the Training and Deployment of Distributed Machine Learning AI Model

Sakthidevi, I., Rajkumar, G. V., Sunitha, R., Sangeetha, A. L., Krishnan, R. S., & Sundararajan, S. (2023) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac58438.2023.10290278 · Cited by 58 · see citing works

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

Nahar, N., Zhang, H., Lewis, G. A., Zhou, S., & Kästner, C. (2023) · IEEE/ACM International Conference on AI Engineering – Software Engineering for AI (CAIN) · doi:10.1109/cain58948.2023.00034 · Cited by 58 · see citing works

Machine Learning Operations in Health Care: A Scoping Review

Rajagopal, A., Ayanian, S., Ryu, A. J., Qian, R., Legler, S. R., Peeler, E. A., Issa, M., Coons, T. J., & Kawamoto, K. (2024) · Mayo Clinic Proceedings: Digital Health · doi:10.1016/j.mcpdig.2024.06.009 · Cited by 52 · see citing works

Role of artificial intelligence in cloud computing, IoT and SDN: Reliability and scalability issues

Belgaum, M. R., Alansari, Z., Musa, S., Alam, M. M., & Mazliham, M. S. (2021) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v11i5.pp4458-4470 · Cited by 46 · see citing works

Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach

Bayram, F., & Ahmed, B. S. (2025) · ACM Computing Surveys · doi:10.1145/3708497 · Cited by 48 · see citing works

A Multivocal Literature Review of MLOps Tools and Features

Recupito, G., Pecorelli, F., Catolino, G., Moreschini, S., Nucci, D. D., Palomba, F., & Tamburri, D. A. (2022) · Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa56994.2022.00021 · Cited by 45 · see citing works

Asset Management in Machine Learning: State-of-research and State-of-practice

Idowu, S., Strüber, D., & Berger, T. (2022) · ACM Computing Surveys · doi:10.1145/3543847 · Cited by 42 · see citing works

Factors influencing readiness for artificial intelligence: a systematic literature review

Ali, W., & Khan, A. Z. (2024) · Data Science and Management · doi:10.1016/j.dsm.2024.09.005 · Cited by 53 · see citing works

Operationalizing machine learning models: a systematic literature review

Kolltveit, A. B., & Li, J. (2022) · Proceedings of the 1st Workshop on Software Engineering for Responsible AI · doi:10.1145/3526073.3527584 · Cited by 34 · see citing works

Extending reference architecture of big data systems towards machine learning in edge computing environments

Pääkkönen, P., & Pakkala, D. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00303-y · Cited by 34 · see citing works

Machine learning operations landscape: platforms and tools

Berberi, L., Kozlov, V. Y., Nguyen, G., Díaz, J. S.-P., Calatrava, A., Moltó, G., Tran, V., & García, Á. L. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11164-3 · Cited by 47 · see citing works

System of Systems Lifecycle Management—A New Concept Based on Process Engineering Methodologies

Kozma, D., Varga, P., & Larrinaga, F. (2021) · Applied Sciences · doi:10.3390/app11083386 · Cited by 32 · see citing works

Devops, A New Approach To Cloud Development & Testing

Agrawal, P., & Rawat, N. (2019) · International Conference on Issues and Challenges in Intelligent Computing Techniques · doi:10.1109/icict46931.2019.8977662 · Cited by 31 · see citing works

Work Systems in the Indian Information Technology (IT) Industry Delivering Artificial Intelligence (AI) Solutions and the Challenges of Work from Home

Venumuddala, V. R., & Kamath, R. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10259-4 · Cited by 29 · see citing works

A Survey on Trusted Distributed Artificial Intelligence

Ağca, M. A., Faye, S., & Khadraoui, D. (2022) · IEEE Access · doi:10.1109/access.2022.3176385 · Cited by 27 · see citing works

Software engineering practices for machine learning — Adoption, effects, and team assessment

Serban, A., van der Blom, K., Hoos, H. H., & Visser, J. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111907 · Cited by 29 · see citing works

Usage of Digital Twins Along a Typical Process Development Cycle.

Sinner, P., Daume, S., Herwig, C., & Kager, J. (2020) · Advances in Biochemical Engineering/Biotechnology · doi:10.1007/10_2020_149 · Cited by 17 · see citing works

Towards a knowledge graph framework for ad hoc analysis in manufacturing

Meyers, B., Vangheluwe, H., Lietaert, P., Vanderhulst, G., Van Noten, J., Schaffers, M., Maes, D., & Gadeyne, K. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02319-6 · Cited by 23 · see citing works

Navigating the DevOps landscape

Zhang, X., Zhao, P., & Jaskolka, J. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112331 · Cited by 21 · see citing works

An empirical study of challenges in machine learning asset management

Zhao, Z., Chen, Y., Bangash, A. A., Adams, B., & Hassan, A. E. (2024) · Empirical Software Engineering · doi:10.1007/s10664-024-10474-4 · Cited by 20 · see citing works

A monitoring framework for deployed machine learning models with supply chain examples

Eck, B. J., Kabakci-Zorlu, D., Chen, Y., Savard, F., & Bao, X. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020394 · Cited by 18 · see citing works

Trust Management for Artificial Intelligence: A Standardization Perspective

Um, T., Kim, J., Lim, S., & Lee, G. M. (2022) · Applied Sciences · doi:10.3390/app12126022 · Cited by 17 · see citing works

Seamless Transition From Machine Learning on the Cloud to Industrial Edge Devices With Thinger.io

Bustamante, Á. L., Patricio, M. A., Berlanga, A., & Molina, J. M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3268771 · Cited by 17 · see citing works

A narrative review of AI-driven predictive maintenance in medical 3D printing

Boretti, A. A. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-14305-0 · Cited by 17 · see citing works

Architecture for Enabling Edge Inference via Model Transfer from Cloud Domain in a Kubernetes Environment

Pääkkönen, P., Pakkala, D., Kiljander, J., & Sarala, R. (2020) · Future Internet · doi:10.3390/fi13010005 · Cited by 16 · see citing works

Edge Intelligence as a Service

Raith, P., & Dustdar, S. (2021) · IEEE International Conference on Services Computing · doi:10.1109/scc53864.2021.00038 · Cited by 12 · see citing works

ModelOps for enhanced decision-making and governance in emergency control rooms

Lefevre, K., Arora, C., Lee, K., Zaslavsky, A., Bouadjenek, M. R., Hassani, A., & Razzak, I. (2022) · Environment Systems & Decisions · doi:10.1007/s10669-022-09855-1 · Cited by 12 · see citing works

CausalOps — Towards an industrial lifecycle for causal probabilistic graphical models

Maier, R., Schlattl, A., Guess, T., & Mottok, J. (2024) · Information and Software Technology · doi:10.1016/j.infsof.2024.107520 · Cited by 12 · see citing works

A Federated Framework for Fine-Grained Cloud Access Control for Intelligent Big Data Analytic by Service Providers

Ra, G.-J., Kim, D., Seo, D., & Lee, I.-Y. (2021) · IEEE Access · doi:10.1109/access.2021.3067958 · Cited by 12 · see citing works

Applied AI matters: AI4Code

Talamadupula, K. (2021) · AI Matters · doi:10.1145/3465074.3465080 · Cited by 11 · see citing works

Extracting enhanced artificial intelligence model metadata from software repositories

Tsay, J., Braz, A., Hirzel, M. J., Shinnar, A., & Mummert, T. (2022) · Empirical Software Engineering · doi:10.1007/s10664-022-10206-6 · Cited by 11 · see citing works

The Impact of Resource Allocation on the Machine Learning Lifecycle

Duda, S., Hofmann, P., Urbach, N., Völter, F., & Zwickel, A. (2023) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00842-7 · Cited by 12 · see citing works

Designing a data mining process for the financial services domain

Plotnikova, V., Dumas, M., Nolte, A., & Milani, F. (2022) · Journal of Business Analytics · doi:10.1080/2573234x.2022.2088412 · Cited by 16 · see citing works

Agility in Software 2.0 – Notebook Interfaces and MLOps with Buttresses and Rebars

Borg, M. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-030-94238-0_1 · Cited by 10 · see citing works

A literature review on cloud based smart transport system

Mobin, G., & Roy, A. (2021) · 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei51242.2021.9452884 · Cited by 9 · see citing works

Railway Cloud Resource Management as a Service

Atanasov, I. I., Dimitrova, D., Pencheva, E. N., & Trifonov, V. G. (2025) · Future Internet · doi:10.3390/fi17050192 · Cited by 10 · see citing works

MLOps in Data Science Projects: A Review

Haertel, C., Staegemann, D. G., Daase, C., Pohl, M., Nahhas, A., & Turowski, K. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386139 · Cited by 9 · see citing works

A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward

Majeed, A., & Hwang, S. O. (2024) · IEEE Access · doi:10.1109/access.2024.3413069 · Cited by 9 · see citing works

SCAFE: A Service-Centered Cloud-Native Workflow Engine Architecture

Ding, Z., Zhou, Y., Wang, S., & Jiang, C. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3259989 · Cited by 6 · see citing works

Artificial intelligence orchestration for text-based ultrasonic simulation via self-review by multi-large language model agents

Kim, S., Yu, Y., & Seo, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-97498-y · Cited by 6 · see citing works

Practices and Infrastructures for Machine Learning Systems: An Interview Study in Finnish Organizations

Muiruri, D., Lwakatare, L. E., Nurminen, J. K., & Mikkonen, T. (2022) · Computer · doi:10.1109/mc.2022.3161161 · Cited by 6 · see citing works

Automation and Development Effort in Continuous AI Development: A Practitioners’ Survey

Steidl, M., Golendukhina, V., Felderer, M., & Ramler, R. (2023) · Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa60479.2023.00027 · Cited by 6 · see citing works

An Exploratory Study on Machine Learning Model Management

Latendresse, J., Abedu, S., Abdellatif, A., & Shihab, E. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3688841 · Cited by 5 · see citing works

Lifecycle Management of Federated Learning Artifacts in Industrial Applications

Hießl, T., Lakani, S. R., Ungersböck, M., Kemnitz, J., Schall, D., & Schulte, S. (2023) · IEEE International Conference on Fog and Edge Computing (ICFEC) · doi:10.1109/icfec57925.2023.00010 · Cited by 5 · see citing works

An Edge Deployment Framework to Scale AI in Industrial Applications

Kemnitz, J., Weißenfeld, A., Schoeffl, L., Stiftinger, A., Rechberger, D., Prangl, B., Kaufmann, T., Hießl, T., Holly, S., Heistracher, C., et al. (2023) · IEEE International Conference on Fog and Edge Computing (ICFEC) · doi:10.1109/icfec57925.2023.00012 · Cited by 5 · see citing works

Operationalizing MLOps with Databricks Pipelines: Scalable Machine Learning in Cloud Environments

Sanepalli, U. R. (2024) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit25113573 · Cited by 4 · see citing works

Explainability e auditability: interpretando e validando modelos de machine learning

Silva, W., Oliveira, V., Araújo, S. S., Vieira, D., & Castro, M. (2022) · doi:10.5753/sbc.11014.1.3 · Cited by 4 · see citing works

Adaptive Method for Machine Learning Model Selection in Data Science Projects

Tavares, C., do Nascimento, N. M., Alencar, P., & Cowan, D. D. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020386 · Cited by 4 · see citing works

A Review on Machine Learning Deployment Patterns and Key Features in the Prediction of Preeclampsia

Pedersen, L., Mazur-Milecka, M., Rumiński, J., & Wagner, S. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6040123 · Cited by 4 · see citing works

Streamlining Compliance and Governance with AI in Cloud-based Programs

-, D. P. (2023) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2023.v05i04.5213 · Cited by 3 · see citing works

PipeSim: Trace-driven Simulation of Large-Scale AI Operations PlatformsPreprint

Rausch, T., Hummer, W., & Muthusamy, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.12587 · Cited by 3 · see citing works

The Role of Artificial Intelligence in Asset Management of Enterprise Systems

Adebayo, A., Sharif, M. S., & Elmedany, W. M. (2022) · International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) · doi:10.1109/3ict56508.2022.9990666 · Cited by 2 · see citing works

Fostering CRM Through Artificial Intelligence

Natrajan, N. S., Singh, S. K., & Sanjeev, R. (2021) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/978-1-7998-7959-6.ch005 · Cited by 3 · see citing works

Fitness Assessment of AI-Based Systems

Borth, M., Filho, J. D. O., & van der Ploeg, C. (2024) · Prognostics and System Health Management Conference · doi:10.1109/phm61473.2024.00050 · Cited by 2 · see citing works

Towards Higher Maturity for Machine Learning: A Conceptual Modelling Approach

Sothilingam, R., Yu, E. S. K., & Senderovich, A. (2020) · The iJournal Student Journal of the Faculty of Information · doi:10.33137/ijournal.v5i1.33476 · Cited by 2 · see citing works

Practices and Infrastructures for ML Systems -- An Interview StudyPreprint

Muiruri, D., Lwakatare, L. E., Nurminen, J. K., & Mikkonen, T. (2021) · doi:10.36227/techrxiv.16939192.v1 · Cited by 2 · see citing works

On Deep Research Problems in Deep LearningPreprint

Ulhaq, A. (2021) · doi:10.31224/osf.io/r78ds · Cited by 2 · see citing works

Sustainable Engineering of Machine Learning-Enabled Systems: A Systematic Mapping StudyPreprint

Chadli, K., Botterweck, G., & Saber, T. (2024) · Research Square · doi:10.21203/rs.3.rs-4694122/v1 · Cited by 2 · see citing works

Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects

Tavares, C., do Nascimento, N. M., Alencar, P., & Cowan, D. D. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386105 · Cited by 1 · see citing works

Algorithmic Innovations: Pioneering the Future of Library Services

Meesad, P., & Mingkhwan, A. (2024) · Studies in big data · doi:10.1007/978-3-031-69216-1_3 · Cited by 2 · see citing works

MLTradeOps: Embedding Trade-Off Management into the MLOps Workflow

Indykov, V., Strüber, D., & Wohlrab, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04190-6_7 · Cited by 1 · see citing works

A systematic survey: Implementation of software life cycle using DevOps

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