{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T01:32:47Z","timestamp":1768267967958,"version":"3.49.0"},"reference-count":56,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>The growing use of machine learning (ML) and artificial intelligence across sectors has shown strong potential to improve decision-making processes. However, the adoption of ML by non-technical professionals remains limited due to the complexity of traditional development workflows, which often require software engineering and data science expertise. In recent years, low-code and no-code platforms have emerged as promising solutions to democratize ML by abstracting many of the technical tasks typically involved in software engineering pipelines. This paper investigates whether these platforms can offer a viable alternative for making ML accessible to non-expert users. Beyond predictive performance, this study also evaluates usability, setup complexity, the transparency of automated workflows, and cost management under realistic \u201cout-of-the-box\u201d conditions. This multidimensional perspective provides insights into the practical viability of LC\/NC tools in real-world contexts. The comparative evaluation was conducted using three leading cloud-based tools: Amazon SageMaker Canvas, Google Cloud Vertex AI, and Azure Machine Learning Studio. These tools employ ensemble-based learning algorithms such as Gradient Boosted Trees, XGBoost, and Random Forests. Unlike traditional ML workflows that require extensive software engineering knowledge and manual optimization, these platforms enable domain experts to build predictive models through visual interfaces. The findings show that all platforms achieved high accuracy, with consistent identification of key features. Google Cloud Vertex AI was the most user-friendly, SageMaker Canvas offered a highly visual interface with some setup complexity, and Azure Machine Learning delivered the best model performance with a steeper learning curve. Cost transparency also varied considerably, with Google Cloud and Azure providing clearer safeguards against unexpected charges compared to Sagemaker Canvas.<\/jats:p>","DOI":"10.3390\/make7040141","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T14:55:15Z","timestamp":1762527315000},"page":"141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Democratizing Machine Learning: A Practical Comparison of Low-Code and No-Code Platforms"],"prefix":"10.3390","volume":"7","author":[{"given":"Luis","family":"Giraldo","sequence":"first","affiliation":[{"name":"Department of Computer Systems and Telematics Engineering, Universidad de Extremadura, 10003 C\u00e1ceres, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8911-9371","authenticated-orcid":false,"given":"Sergio","family":"Laso","sequence":"additional","affiliation":[{"name":"Department of Computer Systems and Telematics Engineering, Universidad de Extremadura, 10003 C\u00e1ceres, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.jisako.2024.01.013","article-title":"Machine learning\/artificial intelligence in sports medicine: State of the art and future directions","volume":"9","author":"Pareek","year":"2024","journal-title":"J. ISAKOS"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101083","DOI":"10.1016\/j.bjpt.2024.101083","article-title":"Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives","volume":"28","author":"Reis","year":"2024","journal-title":"Braz. J. Phys. Ther."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100379","DOI":"10.1016\/j.cosrev.2021.100379","article-title":"A survey on deep learning and its applications","volume":"40","author":"Dong","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Reuther, A., Michaleas, P., Jones, M., Gadepally, V., Samsi, S., and Kepner, J. (2020, January 22\u201324). Survey of machine learning accelerators. Proceedings of the 2020 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA.","DOI":"10.1109\/HPEC43674.2020.9286149"},{"key":"ref_5","unstructured":"Davenport, T.H., and Patil, D. (Harvard Business Review, 2022). Is data scientist still the sexiest job of the 21st century, Harvard Business Review."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, L., and Wu, Z. (2022). How can no\/low code platforms help end-users develop ml applications?\u2014A systematic review. HCI International 2022\u2014Late Breaking Papers: Interacting with eXtended Reality and Artificial Intelligence, Proceedings of the International Conference on Human-Computer Interaction, Virtual, 26 June\u20131 July 2022, Springer.","DOI":"10.1007\/978-3-031-21707-4_25"},{"key":"ref_7","unstructured":"Services, A.W. (2025, August 22). SageMaker Canvas Documentation. Available online: https:\/\/aws.amazon.com\/sagemaker\/canvas\/."},{"key":"ref_8","unstructured":"Cloud, G. (2025, August 22). Vertex AI AutoML Documentation. Available online: https:\/\/cloud.google.com\/vertex-ai\/docs\/."},{"key":"ref_9","unstructured":"Azure, M. (2025, August 22). Azure Machine Learning Studio Documentation. Available online: https:\/\/azure.microsoft.com\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e1496","DOI":"10.1002\/widm.1496","article-title":"Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective","volume":"13","author":"Ghosh","year":"2023","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Munoz-Macho, A.A., Dom\u00ednguez-Morales, M.J., and Sevillano-Ramos, J.L. (2024). Performance and healthcare analysis in elite sports teams using artificial intelligence: A scoping review. Front. Sport. Act. Living, 6.","DOI":"10.3389\/fspor.2024.1383723"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shivashankar, K., and Martini, A. (September, January 21). Maintainability challenges in ML: A systematic literature review. Proceedings of the 2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Gran Canaria, Spain.","DOI":"10.1109\/SEAA56994.2022.00018"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Karmaker Santu, S.K., Hassan, M.M., Smith, M.J., Xu, L., Zhai, C., and Veeramachaneni, K. (2020). AutoML to Date and Beyond: Challenges and Opportunities. arXiv.","DOI":"10.1145\/3470918"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kok, C.L., Tan, H.R., Ho, C.K., Lee, C., Teo, T.H., and Tang, H. (2024, January 16\u201319). A Comparative Study of AI and Low-Code Platforms for SMEs: Insights into Microsoft Power Platform, Google AutoML and Amazon SageMaker. Proceedings of the 2024 IEEE 17th International Symposium on Embedded Multicore\/Many-core Systems-on-Chip (MCSoC), Kuala Lumpur, Malaysia.","DOI":"10.1109\/MCSoC64144.2024.00018"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Simhadri, S.Y. (2025). No-Code vs Traditional Machine Learning for Lead Generation: A Comparative Case Study. Int. J. Emerg. Trends Comput. Sci. Inf. Technol., 118\u2013123.","DOI":"10.56472\/ICCSAIML25-114"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Raghavendran, K.R., and Elragal, A. (2023). Low-code machine learning platforms: A fastlane to digitalization. Informatics, 10.","DOI":"10.20944\/preprints202305.1238.v1"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Azevedo, K., Quaranta, L., Calefato, F., and Kalinowski, M. (2024). A Multivocal Literature Review on the Benefits and Limitations of AutoML. arXiv.","DOI":"10.1016\/j.infsof.2024.107608"},{"key":"ref_18","unstructured":"Pletzl, S., Haberl, A., Ross-Hellauer, T., and Thalmann, S. (2024, January 16\u201319). Reproducible AutoML: An Assessment of Research Reproducibility of No-Code AutoML Tools. Proceedings of the 19th International Conference on Wirtschaftsinformatik, W\u00fcrzburg, Germany."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sun, Y., Song, Q., Gui, X., Ma, F., and Wang, T. (2023). AutoML in the Wild: Obstacles, Workarounds, and Expectations. arXiv.","DOI":"10.1145\/3544548.3581082"},{"key":"ref_20","unstructured":"Arag\u00e3o, A. (2025). A comprehensive comparison of AutoML frameworks across tasks. Sci. Rep., 15."},{"key":"ref_21","unstructured":"Saleh, A., Tarkoma, S., Donta, P.K., Motlagh, N.H., Dustdar, S., Pirttikangas, S., and Lov\u00e9n, L. (2025). Usercentrix: An agentic memory-augmented ai framework for smart spaces. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shi, Z., Dong, J., and Gan, Y. (2025). Democratizing Digital Transformation: A Multisector Study of Low-Code Adoption Patterns, Limitations, and Emerging Paradigms. Appl. Sci., 15.","DOI":"10.3390\/app15126481"},{"key":"ref_23","unstructured":"Bezrukavnikov, O., and Linder, R. (2021). A Neophyte With AutoML: Evaluating the Promises of Automatic Machine Learning Tools. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Truss, M., and Schmitt, M. (2024). Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitations. arXiv.","DOI":"10.2139\/ssrn.4718681"},{"key":"ref_25","first-page":"56","article-title":"Teaching Tip: Using No-Code AI to Teach Machine Learning in Non-Technical Educational Programs","volume":"35","author":"Sundberg","year":"2024","journal-title":"J. Inf. Syst. Educ."},{"key":"ref_26","first-page":"2481","article-title":"Democratizing Artificial Intelligence: How No-Code AI Can Leverage Machine Learning Operations","volume":"32","author":"Sundberg","year":"2023","journal-title":"Bus. Strategy Environ."},{"key":"ref_27","unstructured":"(2024). The Promise and Perils of Low-Code AI Platforms: A Longitudinal Field Study. MIS Q. Exec., 23, 44\u201361."},{"key":"ref_28","unstructured":"D\u2019Aloisio, A. (2025). MANILA: A Low-Code Fairness Benchmarking Platform. arXiv."},{"key":"ref_29","unstructured":"Lehmann, F., and Buschek, D. (2024). Functional Flexibility in Generative AI Interfaces: Text Editing with LLMs through Conversations, Toolbars, and Prompts. arXiv."},{"key":"ref_30","first-page":"26758","article-title":"The Future of Intelligent Automation: How Low-Code\/No-Code Platforms Are Transforming AI Decisioning","volume":"14","author":"Viswanadhapalli","year":"2025","journal-title":"Int. J. Eng. Comput. Sci."},{"key":"ref_31","first-page":"119","article-title":"User Acceptance and Effectiveness of AutoML Systems: The Role of Perceived Ease of Use and Usefulness","volume":"10","author":"Dirgantari","year":"2025","journal-title":"J. Pekommas"},{"key":"ref_32","unstructured":"Microsoft (2025, August 22). Microsoft Azure. Available online: https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/."},{"key":"ref_33","unstructured":"Amazon (2025, August 22). Amazon Web Services. Available online: https:\/\/aws.amazon.com\/."},{"key":"ref_34","unstructured":"Google (2025, August 22). Google Cloud. Available online: https:\/\/cloud.google.com\/."},{"key":"ref_35","unstructured":"Gartner (2025, August 22). Gartner Magic Quadrant for Data Science and Machine Learning Platforms. Available online: https:\/\/www.gartner.com\/en\/documents\/5509595."},{"key":"ref_36","unstructured":"AIMagazine (2025, August 22). Top 10 Data Platforms. Available online: https:\/\/aimagazine.com\/articles\/top-10-data-platforms."},{"key":"ref_37","first-page":"52","article-title":"AutoML: A systematic review on automated machine learning with neural architecture search","volume":"2","author":"Salehin","year":"2024","journal-title":"J. Inf. Intell."},{"key":"ref_38","unstructured":"Kaggle (2025, August 22). Kaggle Dataset. Available online: https:\/\/www.kaggle.com\/."},{"key":"ref_39","unstructured":"Ayessa (2025, August 22). Baseball Players Height or Weight Prediction. Available online: https:\/\/www.kaggle.com\/datasets\/ayessa\/predict-baseball-players-position."},{"key":"ref_40","unstructured":"Mobius (2025, August 22). FitBit Fitness Tracker Data. Available online: https:\/\/www.kaggle.com\/datasets\/arashnic\/fitbit."},{"key":"ref_41","unstructured":"Khorasani, V. (2025, August 22). Gym Members Exercise Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/valakhorasani\/gym-members-exercise-dataset."},{"key":"ref_42","unstructured":"Mrsimple (2025, August 22). Injury Prediction Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/mrsimple07\/injury-prediction-dataset."},{"key":"ref_43","unstructured":"Ziya07 (2025, August 22). College Sports Injury Detection Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/ziya07\/college-sports-injury-detection."},{"key":"ref_44","unstructured":"G\u00e9ron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O\u2019Reilly Media."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Naidu, G., Zuva, T., and Sibanda, E.M. (2023). A review of evaluation metrics in machine learning algorithms. Artificial Intelligence Application in Networks and Systems, Proceedings of the Computer Science On-line Conference, Virtual, 3\u20135 April 2023, Springer.","DOI":"10.1007\/978-3-031-35314-7_2"},{"key":"ref_46","unstructured":"Services, A.W. (2025, August 22). SageMaker Canvas AutoML Data Split Config. Available online: https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/APIReference\/API_AutoMLDataSplitConfig."},{"key":"ref_47","unstructured":"Cloud, G. (2025, August 22). Vertex AI Data Splits for Tabular Data. Available online: https:\/\/cloud.google.com\/vertex-ai\/docs\/tabular-data\/data-splits."},{"key":"ref_48","unstructured":"Azure, M. (2025, August 22). Azure Machine Learning Studio Data Split and Cross-Validation. Available online: https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/how-to-configure-cross-validation-data-splits."},{"key":"ref_49","unstructured":"Nielsen, J. (1995). 10 Usability Heuristics for User Interface Design, Nielsen Norman Group."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Jordan, P.W., Thomas, B., McClelland, I.L., and Weerdmeester, B. (1996). Usability Evaluation in Industry, CRC Press.","DOI":"10.1201\/9781498710411"},{"key":"ref_51","unstructured":"Google Cloud (2025, August 21). Use AI-Powered Prompt Writing Tools in Vertex AI Studio. Available online: https:\/\/cloud.google.com\/vertex-ai\/generative-ai\/docs\/learn\/prompts\/ai-powered-prompt-writing."},{"key":"ref_52","unstructured":"Microsoft Azure (2025, August 22). What Is Azure Machine Learning Prompt Flow. Available online: https:\/\/learn.microsoft.com\/en-us\/azure\/machine-learning\/prompt-flow\/overview-what-is-prompt-flow."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4037","DOI":"10.1109\/TPAMI.2020.2992393","article-title":"Self-Supervised Visual Feature Learning with Deep Neural Networks: A Survey","volume":"43","author":"Jing","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","article-title":"A Survey on Semi-Supervised Learning","volume":"109","author":"Hoos","year":"2020","journal-title":"Mach. Learn."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"82146","DOI":"10.1109\/ACCESS.2021.3084358","article-title":"A Survey on Semi-, Self- and Unsupervised Learning for Image Classification","volume":"9","author":"Schmarje","year":"2021","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A. (2021, January 10\u201317). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00951"}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/141\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T05:12:48Z","timestamp":1762665168000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":56,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["make7040141"],"URL":"https:\/\/doi.org\/10.3390\/make7040141","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,7]]}}}