Developments in MLflow: A System to Accelerate the Machine Learning Lifecycle

XGBoost: A Scalable Tree Boosting System

Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569

TensorFlow: A system for large-scale machine learningPreprint

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,548

Data Structures for Statistical Computing in Python

McKinney, W. (2010) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-92bf1922-00a · Cited by 12,930

Apache Spark: A unified engine for big data processing

Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M. J., et al. (2016) · Communications of the ACM · doi:10.1145/2934664 · Cited by 2,757

Clipper: A Low-Latency Online Prediction Serving System

Crankshaw, D., Wang, X., Zhou, G., Franklin, M. J., Gonzalez, J. E., & Stoica, I. (2016) · arXiv · doi:10.48550/arxiv.1612.03079 · Cited by 415

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

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

Provenance Analysis for Missing Answers and Integrity Repairs.

Zaharia, M., Chen, A., Davidson, A., Ghodsi, A., Hong, S. A., Konwinski, A., Murching, S., Nykodym, T., Ogilvie, P., Parkhe, M., et al. (2018) · IEEE Data(base) Engineering Bulletin · Cited by 112

The Sacred Infrastructure for Computational Research

Greff, K., Klein, A., Chovanec, M., Hutter, F., & Schmidhuber, J. (2017) · Proceedings of the Python in Science Conferences · doi:10.25080/shinma-7f4c6e7-008 · Cited by 63

Data collection and quality challenges in deep learning: a data-centric AI perspective

Whang, S. E., Roh, Y., Song, H., & Lee, J.-G. (2023) · The VLDB Journal · doi:10.1007/s00778-022-00775-9 · Cited by 573

Artificial Intelligence Governance For Businesses

Schneider, J., Abraham, R., Meske, C., & Brocke, J. V. (2022) · Information Systems Management · doi:10.1080/10580530.2022.2085825 · Cited by 273

The Principles of Data-Centric AI

Jarrahi, M. H., Memariani, A., & Guha, S. (2023) · Communications of the ACM · doi:10.1145/3571724 · Cited by 101

Current approaches for executing big data science projects—a systematic literature review

Saltz, J., & Krasteva, I. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.862 · Cited by 65

Enhancing precision agriculture through cloud based transformative crop recommendation model

Singh, G., & Sharma, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-93417-3 · Cited by 50

Machine Learning for Health: Algorithm Auditing & Quality Control

Oala, L., Murchison, A. G., Balachandran, P., Choudhary, S., Fehr, J., Leite, A. W., Goldschmidt, P. G., Johner, C., Schörverth, E. D. M., Nakasi, R., et al. (2021) · Journal of Medical Systems · doi:10.1007/s10916-021-01783-y · Cited by 48

A neural simulation-based inference approach for characterizing the Galactic Center γ-ray excess

Mishra-Sharma, S., & Cranmer, K. (2021) · Physical Review D · doi:10.1103/physrevd.105.063017 · Cited by 44

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

Ensemble of temporal Transformers for financial time series

Olorunnimbe, K., & Viktor, H. L. (2024) · Journal of Intelligent Information Systems · doi:10.1007/s10844-024-00851-2 · Cited by 31

LIMA: Fine-grained Lineage Tracing and Reuse in Machine Learning Systems

Phani, A., Rath, B., & Böehm, M. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3452788 · Cited by 30

Capturing end-to-end provenance for machine learning pipelines

Schlegel, M., & Sattler, K.-U. (2024) · Information Systems · doi:10.1016/j.is.2024.102495 · Cited by 29

Machine Learning With Computer Networks: Techniques, Datasets, and Models

Afifi, H., Pochaba, S., Boltres, A., Laniewski, D., Haberer, J., Paeleke, L., Poorzare, R., Stolpmann, D., Wehner, N., Redder, A., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3384460 · Cited by 36

MLPro 1.0 - Standardized reinforcement learning and game theory in Python

Arend, D., Yuwono, S., Diprasetya, M. R., & Schwung, A. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100341 · Cited by 25

A roadmap to implementing machine learning in healthcare: from concept to practice

Yan, A. P., Guo, L. L., Inoue, J., Arciniegas, S. E., Vettese, E., Wolochacz, A., Crellin‐Parsons, N., Purves, B., Wallace, S., Patel, A., et al. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1462751 · Cited by 24

Optimizing in-memory database engine for AI-powered on-line decision augmentation using persistent memory

Chen, C., Yang, J. J., Lu, M., Wang, T., Zhao, Z. M., Chen, Y., Dai, W., He, B., Wong, W., Wu, G., et al. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3446095.3446102 · Cited by 20

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

A Holistic Framework for AI Systems in Industrial Applications

Kaymakci, C., Wenninger, S., & Sauer, A. (2021) · Lecture notes in information systems and organisation · doi:10.1007/978-3-030-86797-3_6 · Cited by 19

An End-to-End Deep Learning Framework for Fault Detection in Marine Machinery

Rigas, S., Tzouveli, P. K., & Kollias, S. (2024) · Sensors · doi:10.3390/s24165310 · Cited by 18

Perspectives on automated composition of workflows in the life sciences

Lamprecht, A.-L., Palmblad, M., Ison, J., Schwämmle, V., Manir, M. S. A., Altıntaş, İ., Baker, C. J. O., Amor, A. B. H., Capella-Gutiérrez, S., Charonyktakis, P., et al. (2021) · F1000Research · doi:10.12688/f1000research.54159.1 · Cited by 19

An empirical study on ML DevOps adoption trends, efforts, and benefits analysis

Rzig, D. E., Hassan, F., & Kessentini, M. (2022) · Information and Software Technology · doi:10.1016/j.infsof.2022.107037 · Cited by 18

MLOps

Matsui, B. M. A., & Goya, D. (2022) · doi:10.1145/3526073.3527591 · Cited by 17

DLProv: A Data-Centric Support for Deep Learning Workflow Analyses

Pina, D., Chapman, A. P., Kunstmann, L., de Oliveira, D., & Mattoso, M. (2024) · Workshop on Data Management for End-to-End Machine Learning (DEEM) · doi:10.1145/3650203.3663337 · Cited by 17

Machine learning of hidden variables in multiscale fluid simulation

Joglekar, A. S., & Thomas, A. G. R. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/acf81a · Cited by 16

Unsupervised Machine Learning for Data-Driven Rock Mass Classification: Addressing Limitations in Existing Systems Using Drilling Data

Hansen, T. F., & Aarset, A. (2024) · Rock Mechanics and Rock Engineering · doi:10.1007/s00603-024-04280-z · Cited by 20

Underwater Mediterranean image analysis based on the compute continuum paradigm

Ferrari, M., D’Agostino, D., Aguzzi, J., & Marini, S. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107481 · Cited by 14

ExDRa: Exploratory Data Science on Federated Raw Data

Baunsgaard, S., Böehm, M., Chaudhary, A., Derakhshan, B., Geißelsöder, S., Grulich, P. M., Hildebrand, M. S., Innerebner, K., Markl, V., Neubauer, C., et al. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3457549 · Cited by 19

Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey

Ghosh, A., Urry, C., Mishra, A., Perreault-Levasseur, L., Natarajan, P., Sanders, D., Nagai, D., Tian, C., Cappelluti, N., Kartaltepe, J., et al. (2022) · Astrophysical Journal · doi:10.3847/1538-4357/acd546 · Cited by 8

Inferring dark matter substructure with astrometric lensing beyond the power spectrum

Mishra-Sharma, S. (2021) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/ac494a · Cited by 15

Predictive Quality Modeling for Ultra-Short-Pulse Laser Structuring utilizing Machine Learning

Leyendecker, L., Žurić, M., Nazar, M. A., Johannes, K. G., & Schmitt, R. H. (2023) · Procedia CIRP · doi:10.1016/j.procir.2023.03.047 · Cited by 12

DLProv: a suite of provenance services for deep learning workflow analyses

Pina, D., Kunstmann, L., Chapman, A. P., de Oliveira, D., & Mattoso, M. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2985 · Cited by 14

MLOps: Overview of Current State and Future Directions

Bodor, A., Hnida, M., & Daoudi, N. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-26852-6_14 · Cited by 12

Robust errant beam prognostics with conditional modeling for particle accelerators

Rajput, K., Schram, M., Blokland, W., Alanazi, Y., Ramuhalli, P., Zhukov, A., Peters, C., & Vilalta, R. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad2e18 · Cited by 11

Metadata Representations for Queryable Repositories of Machine Learning Models

Li, Z., Kant, H., Hai, R., Katsifodimos, A., Brambilla, M., & Bozzon, A. (2023) · IEEE Access · doi:10.1109/access.2023.3330647 · Cited by 17

65. Cerebro: A Layered Data Platform for Scalable Deep Learning

Kumar, A. K. (2021) · Conference on Innovative Data Systems Research · Cited by 9

Improving Color Mixture Predictions in Ceramics using Data-centric Deep Learning

Souper, T., Morgado, A. C., Marques, A., Silva, I., & Rosado, L. (2023) · International Conference on Machine Learning Technologies (ICMLT) · doi:10.1145/3589883.3589917 · Cited by 8

Full-Stack Machine Learning Development Framework for Energy Industry Applications

Chen, J., Yao, C., Devarakota, P., Sid‐Ahmed, M., Dong, Z., Kanuparthi, S., & Wei, J. (2022) · Abu Dhabi International Petroleum Exhibition & Conference (ADIPEC) · doi:10.2118/210986-ms · Cited by 8

Simplatab: An Automated Machine Learning Framework for Radiomics-Based Bi-Parametric MRI Detection of Clinically Significant Prostate Cancer

Zaridis, D. I., Pezoulas, V. C., Mylona, E., Kalantzopoulos, C., Tachos, N. S., Tsiknakis, N., Matsopoulos, G. K., Regge, D., Papanikolaou, N. K., Tsiknakis, M., et al. (2025) · Bioengineering · doi:10.3390/bioengineering12030242 · Cited by 7

Eggsplorer: a rapid plant–insect resistance determination tool using an automated whitefly egg quantification algorithm

Devi, M. G., Rustia, D. J. A., Braat, L., Swinkels, K., Espinosa, F. F., van Marrewijk, B. M., Hemming, J., & Caarls, L. (2023) · Plant Methods · doi:10.1186/s13007-023-01027-9 · Cited by 7

AERO: Adaptive Edge-Cloud Orchestration With a Sub-1K-Parameter Forecasting Model

Gort, B. J. D., Kibalya, G., & Antonopoulos, A. (2025) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2025.3553100 · Cited by 9

Modyn: Data-Centric Machine Learning Pipeline Orchestration

Böther, M., Robroek, T., Gsteiger, V., Holzinger, R., Ma, X., Tözün, P., & Klimovic, A. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3709705 · Cited by 12

Artificial Intelligence Pipeline for Mammography-Based Breast Cancer Detection: An Integrated Systematic Review and Large-Scale Experimental Validation

Añez, D., Conti, G., Uriarte, J. J., Olmedo, J. J. S., Martínez‐Murillo, R., & Casanova-Carvajal, Ó. (2025) · Medicina · doi:10.3390/medicina61122237 · Cited by 11

AssistML: an approach to manage, recommend and reuse ML solutions

Zacarias, A. G. V., Reimann, P., Weber, C., & Mitschang, B. (2023) · International Journal of Data Science and Analytics · doi:10.1007/s41060-023-00417-5 · Cited by 7

A Software Toolbox for Deploying Deep Learning Decision Support Systems with XAI Capabilities

Nunnari, F., & Sonntag, D. (2021) · ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS) · doi:10.1145/3459926.3464753 · Cited by 6

VeridicalFlow: a Python package for building trustworthy data science pipelines with PCS

Duncan, J., Kapoor, R., Agarwal, A., Singh, C., & Yu, B. (2022) · The Journal of Open Source Software · doi:10.21105/joss.03895 · Cited by 6

Concept and Architecture for Applying Continuous Machine Learning in Multi-Access Routing at Underground Mining Vehicles

Pääkkönen, P., Backman, J., Pakkala, D., Paananen, J., Seppänen, K., & Ahola, K. (2022) · Applied Sciences · doi:10.3390/app122010679 · Cited by 5

VlaPy: A Python package for Eulerian Vlasov-Poisson-Fokker-Planck Simulations

Joglekar, A. S., & Levy, M. (2020) · The Journal of Open Source Software · doi:10.21105/joss.02182 · Cited by 5

AI Governance for Businesses.Preprint

Schneider, J., Abraham, R., & Meske, C. (2020) · arXiv (Cornell University) · Cited by 5

Layer Redundancy Aware DNN Model Repository Planning for Fast Model Download in Edge Cloud

Geng, H., Li, Y., Wang, S., Gu, L., & Zeng, D. (2025) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2025.3591482 · Cited by 4

Outlook towards deployable continual learning for particle accelerators

Rajput, K., Lin, S., Edelen, A. L., Blokland, W., & Schram, M. (2025) · Machine Learning Science and Technology · doi:10.1088/2632-2153/adeb45 · Cited by 4

Unsupervised Process Anomaly Detection Under Industry Constraints in Cyber-Physical Systems Using Convolutional Autoencoder

Goetz, C., & Humm, B. G. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-8493-8_49 · Cited by 4

MLScent: A Tool for Anti-Pattern Detection in ML Projects

Shivashankar, K., & Martini, A. (2025) · IEEE/ACM International Conference on AI Engineering – Software Engineering for AI (CAIN) · doi:10.1109/cain66642.2025.00026 · Cited by 8

Machine Learning Product Line Engineering: A Systematic Reuse Framework

Teki̇nerdoğan, B. (2025) · Machine Learning and Knowledge Extraction · doi:10.3390/make7030058 · Cited by 5

Benchmark of DNN Model Search at Deployment Time

Zhou, L., Jain, A., Wang, Z., Das, A., Yang, Y., & Zou, J. (2022) · International Conference on Scientific and Statistical Database Management · doi:10.1145/3538712.3538725 · Cited by 3

Deep learning based event reconstruction for cyclotron radiation emission spectroscopy

Esfahani, A. A., Böser, S., Buzinsky, N., Carmona-Benitez, M. C., Cervantes, R., Claessens, C., de Viveiros, L., Fertl, M., Formaggio, J. A., Gaison, J. K., et al. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad3ee3 · Cited by 3

Couler: Unified Machine Learning Workflow Optimization in Cloud

Wang, X., Tang, Y., Guo, T., Sang, B., Wu, J., Sha, J., Zhang, K., Qian, J., & Tang, M. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00393 · Cited by 3

A Low Overhead Approach for Automatically Tracking Provenance in Machine Learning Workflows

Hoffmann, N., & Pour, N. E. (2024) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw61312.2024.00092 · Cited by 3

Accelerating container-based deep learning hyperparameter optimization workloads

Liu, R., Wong, D., Lange, D., Larsson, P., Jethava, V., & Zheng, Q. R. (2022) · Workshop on Data Management for End-to-End Machine Learning (DEEM) · doi:10.1145/3533028.3533309 · Cited by 3

Utilizing protein structure graph embeddings to predict the pathogenicity of missense variants

Danner, M., Begemann, M., Elbracht, M., Kurth, I., & Krause, J. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf097 · Cited by 2

Development and prospective evaluation of a machine learning model to predict serious cardiac outcomes among paediatric cardiac inpatients

Arciniegas, S. E., Yan, A. P., Rapoport, A., Jeewa, A., Muhame, R. M., Guo, L., Wolochacz, A., Jessa, K., Cardiff, B., TOMLINSON, G. A., et al. (2025) · BMJ Digital Health & AI · doi:10.1136/bmjdhai-2025-000114 · Cited by 2

BADDADAN: Mechanistic modelling of time-series gene module expression

Noordijk, B., Reinders, M. J. T., van Dijk, A. D. J., & de Ridder, D. (2025) · Quantitative Plant Biology · doi:10.1017/qpb.2025.10017 · Cited by 2

Machine learning-assisted retention time predictions on a cellulose Tris(3,5)-dimethylphenylcarbamate column in polar organic mode

Imre, A., Dombi, G., Dobó, M., Mhammad, A., Ferencz, E., Balogh, B., Vincze, A., Szabó, Z., Balogh, G. T., Rácz, A., et al. (2025) · Analytica Chimica Acta · doi:10.1016/j.aca.2025.344733 · Cited by 4

DRADG: A Dynamic Risk-Adaptive Data Governance Framework for Modern Digital Ecosystems

Gharib, J., & Gahi, Y. (2026) · Information · doi:10.3390/info17010102 · Cited by 3

Multi-Path Fusion: A Hierarchical Machine Learning Approach for Combining Diverse Data Sets for a Forest Monitoring New Observing System

MacKinnon, J., Harding, D., Moussa, M., Brandt, M., Montesano, P., Carroll, M., Wynne, R. H., Thomas, V. A., Huemmrich, F., & Ranson, J. (2023) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss52108.2023.10282678 · Cited by 2

Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within z < 1.4 in the Hyper Supreme-Cam Wide Survey

Tian, C., Urry, C. M., Ghosh, A., Nagai, D., Ananna, T. T., Powell, M. C., Auge, C., Mishra, A., Sanders, D. B., Cappelluti, N., et al. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adaec0 · Cited by 2

The need for a systematic machine-learning process: A proposal via a mobile malware classification case study

Canbek, G. (2021) · doi:10.1109/iscturkey53027.2021.9654378 · Cited by 2

Advanced Notebook: A tool for enhanced Management of Machine Learning models and procedures in the Healthcare Domain

Danciu, G. M., Nicolae, I.-E., Ilie, I., & Nechifor, C. S. (2023) · International Conference on Applied Mathematics & Computer Science (ICAMCS) · doi:10.1109/icamcs59110.2023.00013 · Cited by 2

Mantik: A Workflow Platform for the Development of Artificial Intelligence on High-Performance Computing Infrastructures

Seidler, T., Emmerich, F., Ehlert, K., Berner, R., Nagel-Kanzler, O., Schultz, N., Quade, M., Schultz, M. G., & Abel, M. (2024) · The Journal of Open Source Software · doi:10.21105/joss.06136 · Cited by 2

Agile MLOps: Bridging the Gap Between Agility and Machine Learning Operations

Papageorgiou, A. V., Symeonidis, G., Nerantzis, E., & Papakostas, G. A. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-96231-8_2 · Cited by 1

Real-Time Out of Distribution Detection in 2D Object Detection for Autonomous Cars

Szabó, L., Weltsch, Z., & Major, A. Á. (2025) · Engineering Perspective · doi:10.64808/engineeringperspective.1814718 · Cited by 1

Dissociative Electron Attachment Prediction of Halogenated Organic Molecules Using Machine Learning

Silva, T., Lobo, V. J. A. S., Pereira‐da‐Silva, J., & da Silva, F. F. (2026) · ACS Omega · doi:10.1021/acsomega.5c13274 · Cited by 1

Software Development Process Model for Machine Learning and Evolving Variants: A Systematic Literature Review

Jamal, S., Ahmad, A., & ZESHAN, D. F. (2026) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194026300010 · Cited by 1

ProSPECT: Proactive Storage Using Provenance for Efficient Compute and Tiering

Murugan, M., Bhattacharya, S., Voigt, D., Bharde, M., & Tom, A. S. (2021) · Transactions of Indian National Academy of Engineering · doi:10.1007/s41403-021-00261-8 · Cited by 1

(Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse

Niemann, M., Assenmacher, D., Brunk, J., Riehle, D. M., Trautmann, H., & Becker, J. (2022) · Aktivismus- und Propagandaforschung · doi:10.1007/978-3-658-35658-3_13 · Cited by 2

Machine Learning Airport Surface Model

Coupe, W. J., Amblard, A., Youlton, S., & Kistler, M. S. (2023) · IEEE/AIAA Digital Avionics Systems Conference (DASC) · doi:10.1109/dasc58513.2023.10311105 · Cited by 1

Leveraging Clinical Data Treasures: Integration of an AI Platform into Clinical IT

Arshad, K., Ardalan, S., Schreiweis, B., & Bergh, B. (2024) · Studies in health technology and informatics · doi:10.3233/shti240619 · Cited by 1

Multimodal Deep Learning for Functional Outcome Prediction in Endovascular Therapy

Nijenhuis, F. G. T., Su, R., van Doormaal, P., Hofmeijer, J., Martens, J. M., van Zwam, W. H., van der Lugt, A., & van Walsum, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-76160-7_14 · Cited by 1

Guiding Word Equation Solving Using Graph Neural Networks

Abdulla, P. A., Atig, M. F., Cailler, J., Liang, C., & Rümmer, P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-78709-6_14 · Cited by 1

Research and Development of Kubernetes Operator for Machine Learning Pipelines

Trapezin, A., & Filianin, I. (2025) · International Russian Smart Industry Conference (SmartIndustryCon) · doi:10.1109/smartindustrycon65166.2025.10985995

Machine Learning Model Management using Cloud-Native Technologies for IoT

Minh, H. N. (2021) · Aaltodoc (Aalto University)

Peer Review #2 of "Current approaches for executing big data science projects—a systematic literature review (v0.1)"

(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.862v0.1/reviews/2

Peer Review #1 of "Current approaches for executing big data science projects—a systematic literature review (v0.1)"

(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.862v0.1/reviews/1

ADaPT-ML: A Data Programming Template for Machine Learning

Whittaker, A. M. (2022) · The Journal of Open Source Software · doi:10.21105/joss.04038

Examining Engineers’ Lived Experiences Deploying Machine Learning Production Models: A Phenomenological Study

Papineni, D. D., & Lind, M. L. (2023) · Journal of Strategic Innovation and Sustainability · doi:10.33423/jsis.v18i1.6058

Life Science Workflow Services (LifeSWS): Motivations and Architecture

Akbarinia, R., Botella, C., Joly, A., Masséglia, F., Mattoso, M., Ogasawara, E., de Oliveira, D., Pacitti, E., Porto, F. A. M., Pradal, C., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-662-68100-8_1

Scalable and Portable Federated Learning Simulation Engine

Galende, B. A., Naranjo, J. M., & Uribe, S. (2023) · doi:10.1145/3624486.3624488

Function+Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning

de Conto, E., Genest, B., & Easwaran, A. (2024) · ACM International Conference on AI-Powered Software (AIware) · doi:10.1145/3664646.3664759

Model Share AI

Peters, H., & Parrott, M. (2024) · Proceedings of the Python in Science Conferences · doi:10.25080/mdce8355

Evaluating Custom-Precision Operator Support in MLIR for ARM CPUs

Mücke, M., & Gratl, C. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74643-7_12

Advancing HCI with Neuromorphic Technology: Guidelines for Designing User-Friendly Developer Tools for Neuromorphic Development

Upreti, D., Maheshwari, A., Tabb, T., Polykretis, I., Gallo, E. M., Stewart, K. M., LaToza, T. D., & Danielescu, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713249 · Cited by 2

From Acquiring to Suggesting DL Design Choices with Agility: A System Design

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