[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118121-en":3,"doc-seo-118121-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118121,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Towards MLOps - A DevOps Tools Recommender System for Machine Learning Systems","Applying DevOps practices to machine learning systems leads to MLOps, where models evolve with new data and must be continuously built, trained, versioned, and deployed. The work links open-source tools into an automated pipeline that creates datasets, trains models, deploys them to production, and stores model and dataset versions. Because selecting an appropriate toolchain is challenging, the paper proposes a recommendation framework using project contextual information, then evaluates it using multiple ML approaches, with random forest achieving the best f-score of 0.66.","Towards MLOps: A DevOps Tools Recommender System for Machine Learning Systems .  \nPir Sami Ullah Shah Department of Software Engineering National University of Computer and Emerging Sciences. Islamabad, Pakistan..  \nNaveed Ahmad  \nDepartment of Software Engineering National University of Computer and Emerging Sciences. Islamabad, Pakistan..  \nMirza Omer Beg  \nDepartment of Artificial Intelligence National University of Computer and Emerging Sciences. Islamabad, Pakistan.  \nAbstract—Applying DevOps practices to machine learning system is termed as MLOps and machine learning systems evolve on new data unlike traditional systems on requirements. The objective of MLOps is to establish a connection between different open-source tools to construct a pipeline that can automatically perform steps to construct a dataset, train the machine learning model and deploy the model to the production as well as store different versions of model and dataset. Benefits of MLOps is to make sure the fast delivery of the new trained models to the production to have accurate results. Furthermore, MLOps practice impacts the overall quality of the software products and is completely dependent on open-source tools and selection of relevant open-source tools is considered as challenged while a generalized method to select an appropriate open-source tools is desirable. In this paper, we present a framework for recommendation system that processes the contextual information (e.g., nature of data, type of the data) of the machine learning project and recommends a relevant toolchain (tech-stack) for the operationalization of machine learning systems. To check the applicability of the proposed framework, four different approaches i.e., rule-based, random forest, decision trees and k-nearest neighbors were investigated where precision, recall and f-score is measured, the random forest out classed other approaches with highest f-score value of 0.66.  \nKeywords—Recommender System, MLOps, DevOps, MLOps pipeline, DevOps Tools.  \nI. INTRODUCTION  \nApplying DevOps practice to machine learning systems is termed as MLOps. Machine Learning Operations (MLOps) is the combination of practices, tools, and techniques that help organizations manage and deploy machine learning models into production environments. It aims to bridge the gap between data scientists and IT professionals, making it easier to deploy and maintain machine learning models in production. The origins of MLOps can be traced back to the early days of machine learning, when data scientists were responsible for creating and deploying machine learning models. However, as machine learning technology has become more prevalent, the need for a more streamlined and efficient approach to deploying models has become apparent. MLOps emerged as a response to this need, focusing on the integration of machine learning models into production environments and the management of these models over time. It involves the use of automation, continuous integration and delivery, and monitoring to ensure that machine learning models are reliable and efficient.  \nHowever, one can face multiple challenges in the construction of MLOps pipeline to deploy machine learning models into production environments. The complete MLOps process and its implementation; is described [1] . MLOps  \npipeline can be constructed by the combination of open-source tools and the availability of open-source tools is abundant [2] and the number of the tools is expected to be increased further. However, selecting a relevant tech-stack for the construction of DevOps pipeline; is considered as challenge [2]–[6] and sometimes becomes cumbersome while the quality of software products also depends on the DevOps pipeline [6] and it is still desirable to have a generalized guidelines to select appropriate open source tools.  \nThis paper presents a recommender system that recommends open-source tools to construct MLOps pipeline that can automatically train, test and","cbCaiqCWBntsTI7w","https://ap.wps.com/l/cbCaiqCWBntsTI7w","pdf",725819,1,7,"English","en",105,"# Introduction\n## Literature Review\n### MLOps Proposed Frameworks\n## Methodology\n## Results and Evaluation\n# Conclusion","[{\"question\":\"What problem does MLOps address compared with traditional machine learning systems?\",\"answer\":\"MLOps applies DevOps practices to machine learning systems, where requirements and model behavior change with new data. It focuses on automating dataset construction, training, deployment, and ongoing management in production.\"},{\"question\":\"How does the proposed recommender system select MLOps tools?\",\"answer\":\"The system recommends an appropriate toolchain based on contextual user inputs such as the nature of data and data type. It also provides details of where each tool can be used across MLOps phases.\"},{\"question\":\"Which evaluation approaches were tested and what was the best result?\",\"answer\":\"The paper investigates rule-based methods, random forest, decision trees, and k-nearest neighbors. Random forest outperformed others with the highest f-score value of 0.66.\"}]","Towards MLOps - A DevOps Tools Recommender System for Machine Learning Systems | PDF",1785681711,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"towards-mlops-a-devops-tools-recommender-system-for-machine-learning-systems","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/towards-mlops-a-devops-tools-recommender-system-for-machine-learning-systems/118121/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does MLOps address compared with traditional machine learning systems?","Question",{"text":75,"@type":76},"MLOps applies DevOps practices to machine learning systems, where requirements and model behavior change with new data. It focuses on automating dataset construction, training, deployment, and ongoing management in production.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed recommender system select MLOps tools?",{"text":80,"@type":76},"The system recommends an appropriate toolchain based on contextual user inputs such as the nature of data and data type. It also provides details of where each tool can be used across MLOps phases.",{"name":82,"@type":73,"acceptedAnswer":83},"Which evaluation approaches were tested and what was the best result?",{"text":84,"@type":76},"The paper investigates rule-based methods, random forest, decision trees, and k-nearest neighbors. 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