[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118392-en":3,"doc-seo-118392-105":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},118392,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine learning operations landscape - platforms and tools","Machine learning operations (MLOps) is becoming essential for managing and monitoring intelligent models once deployed. As organizations increasingly use artificial intelligence as a strategic capability, the need grows for reliable, scalable MLOps platforms across the full machine-learning life cycle. The research provides a comprehensive assessment framework covering key capabilities, including workflow orchestration, experiment tracking, model deployment, and inference. Sixteen widely used MLOps tools are evaluated using feature analysis, GitHub star growth for adoption and prominence, and weighted scoring, producing insights and a decision flowchart to support platform selection.","Machine learning operations landscape: platforms and tools  \nLisana Berberi1 · Valentin Kozlov1 · Giang Nguyen3,5 · Judith Sáinz-Pardo Díaz2 · Amanda Calatrava4 · Germán Moltó4 · Viet Tran3 · Álvaro López García2  \nAccepted: 19 February 2025 © The Author(s) 2025  \nAbstract  \nAs the field of machine learning advances, managing and monitoring intelligent models in production, also known as machine learning operations (MLOps), has become essential. Organizations are increasingly adopting artificial intelligence as a strategic tool, thus increasing the need for reliable, and scalable MLOps platforms. Consequently, every aspect of the machine learning life cycle, from workflow orchestration to performance monitoring, presents both challenges and opportunities that require sophisticated, flexible, and scalable technological solutions. This research addresses this demand by providing a comprehensive assessment framework of MLOps platforms highlighting the key features necessary for a robust MLOps solution. The paper examines 16 MLOps tools widely used, which revolve around capabilities within AI infrastructure management, including but not limited to experiment tracking, model deployment, and model inference. Our three-step evaluation framework starts with a feature analysis of the MLOps platforms, then GitHub stars growth assessment for adoption and prominence, and finally, a weighted scoring method to single out the most influential platforms. From this process, we derive valuable insights into the essential components of effective MLOps systems and provide a decisionmaking flowchart that simplifies platform selection. This framework provides hands-on guidance for organizations looking to initiate or enhance their MLOps strategies, whether they require an end-end solutions or specialized tools.  \nKeywords Machine learning operations · MLOps platforms · Performance monitoring · Decision-making  \n1 Introduction  \nArtificial Intelligence (AI)/Machine Learning (ML) is progressively being included as a crucial solution in the design of new software systems across different industries. However, these ML-based systems bring new challenges ofthe software development process as compared to the ones we have been familiar with. For example, there are no model and/or data versions in the manual ML pipeline, no model lineage available, no tracking of different AI/  \nExtended author information available on the last page of the article  \n1 3  \nML experiment-runs in an automatic way, no performance monitoring of models in production. Therefore, manual processes to create and control ML systems can cause additional costs, lack of reproducibility, and future problems in maintaining them (Rufet al. 2021) .  \nFurthermore, successful development requires collaboration between various specialists with different sets of skills and tools: software developers, data scientists, and ML engineers (Diaz-de Arcaya et al. 2023) .  \nThese challenges can be addressed through the following approaches: Automated ML (AutoML) is an attempt to solve the problem of expertise by providing fully automated offthe-shelf solutions for model choice and hyperparameter tuning. It aims to increase the userfriendliness of ML frameworks to make them more accessible to the nonexpert (Schmitt 2023). In addition, Machine Learning Operations (MLOps) complements this by integrating automated workflows into continuous training pipelines, enabling periodic retraining, monitoring, and feedback loops to maintain model quality and ensure scalability, reproducibility, and operational success (Kreuzberger et al. 2023) .  \nIn the domain of MLOps, continuous monitoring of model and data behavior is crucial for detecting potential performance degradations (Hu et al. 2020) . Deployed ML models, trained on historical data, are susceptible to accuracy challenges when real-world data distributions change-a phenomenon known as data drift. Beyond data distribution shifts, concept drift can occur when","cbCaivg3vh3hE5W0","https://ap.wps.com/l/cbCaivg3vh3hE5W0","pdf",3976416,1,37,"English","en",105,"# Introduction\n## Challenges in ML development without MLOps\n## Approaches: AutoML and MLOps in continuous training\n## Monitoring needs: data drift and concept drift\n## Need for an evaluation framework and platform selection","[{\"question\":\"What problem does MLOps address in production ML systems?\",\"answer\":\"MLOps addresses the need to manage and monitor models in production, integrating reliable workflows for periodic retraining, performance monitoring, and feedback loops to maintain quality and scalability.\"},{\"question\":\"Which core components does the paper evaluate in MLOps platforms?\",\"answer\":\"The paper focuses on capabilities across the ML lifecycle, including experiment tracking, model deployment, model inference, orchestration, and model performance monitoring, along with drift-related functionality.\"},{\"question\":\"How does the proposed framework rank and select MLOps platforms?\",\"answer\":\"It uses a three-step process: feature analysis of the platforms, assessment of GitHub star growth to estimate adoption and prominence, and a weighted scoring method to identify the most influential tools, ending with a decision flowchart.\"}]","Machine learning operations landscape - platforms and tools | PDF",1785683411,93,{"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},"machine-learning-operations-landscape-platforms-and-tools","",{"@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/machine-learning-operations-landscape-platforms-and-tools/118392/",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 in production ML systems?","Question",{"text":75,"@type":76},"MLOps addresses the need to manage and monitor models in production, integrating reliable workflows for periodic retraining, performance monitoring, and feedback loops to maintain quality and scalability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which core components does the paper evaluate in MLOps platforms?",{"text":80,"@type":76},"The paper focuses on capabilities across the ML lifecycle, including experiment tracking, model deployment, model inference, orchestration, and model performance monitoring, along with drift-related functionality.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed framework rank and select MLOps platforms?",{"text":84,"@type":76},"It uses a three-step process: feature analysis of the platforms, assessment of GitHub star growth to estimate adoption and prominence, and a weighted scoring method to identify the most influential tools, ending with a decision flowchart.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]