[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117014-en":3,"doc-seo-117014-105":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},117014,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","Machine Learning Models Monitoring in MLOps Context: Metrics and Tools","Lack of effective monitoring in machine learning projects creates quality, reliability, and sustainability risks once models are deployed in production. As ML becomes central across many application areas, weak monitoring blocks full realization of system value. The article delivers a practical guide to monitoring metrics and tools in an MLOps setting, explaining how metric monitoring supports validation and real-time assessment across development and deployment. It also compares available tools to help organizations maintain consistent model performance.","JIM International Journal of  \nInteractive Mobile Technologies  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJIM | eISSN: 1865-7923 | Vol. 17 No. 23 (2023) |   \n[https://doi.org/10.3991/ijim.v17i23.43479](https://doi.org/10.3991/ijim.v17i23.43479)  \nPAPER  \nMachine Learning Models Monitoring in MLOps Context: Metrics and Tools  \nAnas Bodor1(􀀍), Meriem Hnida1,2, Najima Daoudi1,3  \n1ITQAN Team, LyRica Lab, Information Sciences School, Rabat, Morocco  \n2RIME Team, Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco  \n3SSLab, ENSIAS, Mohammed V University, Rabat, Morocco  \n[anas.bodor@esi.ac.ma](anas.bodor@esi.ac.ma)  \nABSTRACT  \nIn many machine learning projects, the lack of an effective monitoring system is a worrying issue. This leads to a series of challenges and risks that compromise the quality, reliability and sustainability of models deployed in production. As Machine Learning gains importance in various fields, poorly implemented monitoring represents a major obstacle to realizing its full potential. This article presents a comprehensive guide of machine learning models monitoring metricsand tool used in the MLOps context. The monitoring of metrics is important to evaluate and validate the performance of a machine-learning model, not only throughout the development phase but also during its deployment in the production environment. It enables real-time data to be collected on various metrics. The purpose of monitoring in MLOps context is to identify potential issues and adjustments made accordingly, guaranteeing consistent model quality and reliability. This article provides a comprehensive guide that introduces and explains a wide range of metrics used for continuous monitoring of ML systems at various stages of the MLOps lifecycle. Additionally, it presents a comparative analysis of available monitoring tools, enabling organizations to optimize their performance and ensure the seamless deployment of their machine learning applications. In essence, it underscores the critical importance of continuous monitoring and tailored metrics for ensuring the success and reliability of machine learning systems.  \nKEYWORDS  \nmachine learning, MLOps, metrics, monitroring tools, continuous monitoring  \n1 INTRODUCTION  \nMachine learning (ML) has become an integral part of many fields, with itspotential for improving performance and making the right decisions. Technology has seen growing adoption, fueled by increased computing power, more data, and algorithmic advances. However, to fully exploit the benefits of ML, it is essential to put in place robust industrialization processes. Industrialization enables ML models to be transformed into concrete products or services that can be integrated into existing systems and deployed on a large scale [27][28][29] .  \nBodor, A., Hnida, M., Daoudi, N. (2023) . Machine Learning Models Monitoring in MLOps Context: Metrics and Tools. International Journal of Interactive  \nMobile Technologies (iJIM), 17(23), pp. 125–139. [https://doi.org/10.3991/ijim.v17i23.43479](https://doi.org/10.3991/ijim.v17i23.43479)[ ](https://doi.org/10.3991/ijim.v17i23.43479)[Article submitted 2023-07-26. Revision uploaded 2023-10-22. Final acceptance 2023-10-22.](Article submitted 2023-07-26. Revision uploaded 2023-10-22. Final acceptance 2023-10-22.)© 2023 by the authors of this article. Published under CC-BY.  \niJIM | Vol. 17 No. 23 (2023) International Journal of Interactive Mobile Technologies (iJIM) 125  \nBodor et al.  \nDespite significant advances in the field of artificial intelligence the rate of industrialization and deployment to production of ML projects is still low, and the transition of ML models from development stage to production is time consuming, depending on the requirements and particularities of each project. This is supported by the results of a survey carried out in 2020 by Sasu Makineth et al [1], which shows th","cbCaipxomtDc4I1W","https://ap.wps.com/l/cbCaipxomtDc4I1W","pdf",2754901,1,15,"English","en",105,"# Introduction\n# MLOps and Monitoring in Production\n## Monitoring metrics across the MLOps lifecycle\n## Challenges in ML model deployment\n# Metrics for model health assessment\n## Performance evaluation during development and deployment\n# Monitoring tools comparison\n## Tool selection for continuous monitoring","[{\"question\":\"Why is monitoring critical for machine learning models in production?\",\"answer\":\"Because insufficient monitoring introduces risks that degrade the quality, reliability, and long-term sustainability of deployed models. It also makes it harder to detect issues early and maintain performance over time.\"},{\"question\":\"What is the role of monitoring metrics in the MLOps context?\",\"answer\":\"Monitoring metrics evaluates and validates model performance not only during development but also after deployment in production. It enables real-time collection of signals to identify issues and guide adjustments accordingly.\"},{\"question\":\"Which tools are discussed for continuous monitoring of ML systems?\",\"answer\":\"The document presents and compares multiple monitoring tools used in the MLOps context. It aims to help organizations choose tools that optimize performance and support seamless deployment.\"}]","Machine Learning Models Monitoring in MLOps Context: Metrics and Tools | PDF",1785673087,38,{"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-models-monitoring-in-mlops-context-metrics-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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-models-monitoring-in-mlops-context-metrics-and-tools/117014/",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},"Why is monitoring critical for machine learning models in production?","Question",{"text":75,"@type":76},"Because insufficient monitoring introduces risks that degrade the quality, reliability, and long-term sustainability of deployed models. It also makes it harder to detect issues early and maintain performance over time.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the role of monitoring metrics in the MLOps context?",{"text":80,"@type":76},"Monitoring metrics evaluates and validates model performance not only during development but also after deployment in production. It enables real-time collection of signals to identify issues and guide adjustments accordingly.",{"name":82,"@type":73,"acceptedAnswer":83},"Which tools are discussed for continuous monitoring of ML systems?",{"text":84,"@type":76},"The document presents and compares multiple monitoring tools used in the MLOps context. 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