{"id":172011,"date":"2023-10-15T14:00:43","date_gmt":"2023-10-15T13:00:43","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=172011"},"modified":"2023-10-15T14:05:52","modified_gmt":"2023-10-15T13:05:52","slug":"mlops-devops-applied-to-machine-learning-projects","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects","title":{"rendered":"MLOps: DevOps applied to Machine Learning projects"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"172011\" class=\"elementor elementor-172011\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-217b29fc elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"217b29fc\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-305ec835\" data-id=\"305ec835\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7757d683 elementor-widget elementor-widget-heading\" data-id=\"7757d683\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">In a previous article, we presented the DevOps philosophy, and how this new approach enables faster value delivery for businesses through the unification of development (Dev) and operations (Ops) teams, which previously worked in silos. In this article, we'll look at the application of this approach to Machine Learning problems: we're talking about MLOps.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-dac86f6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"dac86f6\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5e1c7765\" data-id=\"5e1c7765\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-20a58712 elementor-widget elementor-widget-heading\" data-id=\"20a58712\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Back to Devops<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d31bcd1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d31bcd1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-55b3721a\" data-id=\"55b3721a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-53a6cf4d elementor-widget elementor-widget-text-editor\" data-id=\"53a6cf4d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><a href=\"https:\/\/datascientest.com\/en\/devops-certification-what-is-it-and-how-do-i-get-it\">DevOps reduces time-to-market<\/a> and improves the quality of software products, thanks to 3 principles:<\/p><ol><li><strong>Disengagement:<\/strong> development and operations teams must work together towards a common goal, sharing all information.<\/li><li><strong>Automation:<\/strong> Everything that can be automated must be: build, test, deployment. This has the dual aim of reducing the time needed for deployment and the number of non-quality issues, as manual steps are prone to human error.<\/li><li><strong>Monitoring<\/strong>: collect and monitor important metrics, both on the business side (number of users connected, number of orders, etc.) and on the operations side (percentage of server CPU \/ RAM utilization, number of errors, etc.).<\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-556f5c8e elementor-widget elementor-widget-image\" data-id=\"556f5c8e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"600\" height=\"324\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/Image-26-04-2021-a-14.03.jpg\" class=\"attachment-large size-large wp-image-172014\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/Image-26-04-2021-a-14.03.jpg 600w, https:\/\/datascientest.com\/en\/files\/2023\/10\/Image-26-04-2021-a-14.03-300x162.jpg 300w\" sizes=\"(max-width: 600px) 100vw, 600px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-71208dec elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"71208dec\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/ml-ops-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover DataScientest's MLOps training course<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-472c57db elementor-widget elementor-widget-text-editor\" data-id=\"472c57db\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Following the <a href=\"https:\/\/datascientest.com\/en\/lean-devops-applying-agile-methodology-to-it-for-efficiency\">massive adoption of DevOps and the significant increase<\/a> in the <a href=\"https:\/\/datascientest.com\/en\/survival-analysis-beyond-machine-learning\">use of Machine Learning<\/a> and AI in the enterprise, DevOps concepts have been taken up in data projects. While the objectives and principles remain broadly the same, certain specific features of machine learning have necessitated the creation of a specific approach: MLOps.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7994ff4a elementor-widget elementor-widget-heading\" data-id=\"7994ff4a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is MLOps?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-59b6d49d elementor-widget elementor-widget-text-editor\" data-id=\"59b6d49d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>According to a <a href=\"https:\/\/cloud.google.com\/architecture\/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning?hl=de\">google documentation:<\/a><\/p><p>MLOps is an ML engineering culture and practice that aims to unify the development (Dev) and operations (Ops) of ML systems.<\/p><p>To understand what MLOps is, let&#8217;s focus on the differences with classic DevOps:<\/p><ul><li><strong>Experimental development:<\/strong> by nature, all ML projects follow a non-linear development path: different preprocessing, <a href=\"https:\/\/datascientest.com\/en\/feature-engineering-importance-for-machine-learning\">feature engineering<\/a> and algorithms are tested, until sufficient performance is achieved. Keeping track of what has and hasn&#8217;t worked can be complex.<\/li><li>But it&#8217;s crucial for reducing development time, especially in the case of large teams, to avoid retesting approaches that have already been tried and failed by another data scientist.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7e193663 elementor-widget elementor-widget-image\" data-id=\"7e193663\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"300\" height=\"298\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/Image-26-04-2021-a-14.58-300x298.jpg\" class=\"attachment-medium size-medium wp-image-172015\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/Image-26-04-2021-a-14.58-300x298.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/10\/Image-26-04-2021-a-14.58-1024x1017.jpg 1024w, https:\/\/datascientest.com\/en\/files\/2023\/10\/Image-26-04-2021-a-14.58-150x150.jpg 150w, https:\/\/datascientest.com\/en\/files\/2023\/10\/Image-26-04-2021-a-14.58-768x763.jpg 768w, https:\/\/datascientest.com\/en\/files\/2023\/10\/Image-26-04-2021-a-14.58.jpg 1089w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Steps in developing a machine learning model<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-51675a17 elementor-widget elementor-widget-text-editor\" data-id=\"51675a17\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul><li><strong>Conceptual drift<\/strong>: unlike a conventional software product, an ML system can see its performance vary (and most of the time decline) over time, without any change in the code or infrastructure. This is due to the phenomenon of conceptual drift, in which the relationships between the variable to be predicted and the explanatory variables will evolve in unforeseen ways.<\/li><li>It is therefore essential to monitor the performance of ML models in production, and potentially to implement an automatic re-learning strategy to &#8220;re-calibrate&#8221; the model when its performance declines. A case in point is Google Flu Trends, a Google project which was designed to estimate the number of cases of influenza based on searches made on the search engine, but which showed its limitations by significantly overestimating the number of cases after a few years of operation.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-36a02dbc elementor-widget elementor-widget-image\" data-id=\"36a02dbc\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"196\" height=\"300\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/Image-26-04-2021-a-15.06-196x300.jpg\" class=\"attachment-medium size-medium wp-image-172016\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/Image-26-04-2021-a-15.06-196x300.jpg 196w, https:\/\/datascientest.com\/en\/files\/2023\/10\/Image-26-04-2021-a-15.06.jpg 500w\" sizes=\"(max-width: 196px) 100vw, 196px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1bbd57d9 elementor-widget elementor-widget-text-editor\" data-id=\"1bbd57d9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul><li style=\"font-weight: 400;\" aria-level=\"1\"><p><strong>Team:<\/strong> In most organizations, cross-functional data teams or teams integrated into different business departments are relatively young, and lack the qualified resources to manage the deployment and maintenance of complex ML systems.<\/p><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><p>Indeed, these teams tend to consist mainly of <a href=\"https:\/\/datascientest.com\/en\/how-to-become-a-data-scientist-the-the-best-kept-secrets\">data scientists who focus on the development<\/a> of machine learning models, but lack the necessary skills to manage the deployment and maintenance of complete applications. What&#8217;s more, data teams still too often operate in silos, without communicating with the various technical teams with whom they would need to interact to put their models into production.<\/p><p>These specificities and complexities inherent to data projects partly explain why, while more and more companies are investing massively in machine learning and AI, very few models are actually deployed in production. According to a study conducted by Algorithmia in 2020, 55% of companies committed to AI have never carried out a production run.<\/p><p>The aim of MLOps is therefore to overcome these difficulties and enable ML systems to be deployed and run, applying principles similar to DevOps:<\/p><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-63376f03 elementor-widget elementor-widget-text-editor\" data-id=\"63376f03\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul><li aria-level=\"1\"><p><strong>Monitoring:<\/strong> monitoring is becoming even more important in the MLOps philosophy, both in the run (notably by collecting model performance metrics to detect conceptual drift) and at the development stage (to track the various experiments and their results).<\/p><\/li><li aria-level=\"1\"><p><strong>Culture<\/strong>: data teams mqust also work with other technical teams (operations, but also the teams developing the software products with <a href=\"https:\/\/datascientest.com\/en\/data-poisoning-a-threat-to-machine-learning-models\">which the ML models are to be integrated).<\/a><\/p><\/li><li aria-level=\"1\"><p><strong>Automation:<\/strong> in addition to the classic stages (build, test, deploy), MLOps advocates the automation of tasks specific to the maintenance of ML systems, such as relearning models. Once models have been trained, they must also be automatically deployed.<\/p><p>To help data teams adopt an MLOps approach, a growing number of tools have been developed. These include MLflow, Metaflow and Kubeflow.<\/p><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5e2c9ba8 elementor-widget__width-initial elementor-widget elementor-widget-video\" data-id=\"5e2c9ba8\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/ugALxRuTh00&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-video .elementor-widget-container{overflow:hidden;transform:translateZ(0)}.elementor-widget-video .elementor-wrapper{aspect-ratio:var(--video-aspect-ratio)}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{height:100%;width:100%;display:flex;border:none;background-color:#000}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-wrapper{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .elementor-open-inline .elementor-custom-embed-image-overlay{position:absolute;top:0;right:0;bottom:0;left:0;background-size:cover;background-position:50%}.elementor-widget-video .elementor-custom-embed-image-overlay{cursor:pointer;text-align:center}.elementor-widget-video .elementor-custom-embed-image-overlay:hover .elementor-custom-embed-play i{opacity:1}.elementor-widget-video .elementor-custom-embed-image-overlay img{display:block;width:100%;aspect-ratio:var(--video-aspect-ratio);-o-object-fit:cover;object-fit:cover;-o-object-position:center center;object-position:center center}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-custom-embed-image-overlay{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-custom-embed-image-overlay img{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .e-hosted-video .elementor-video{-o-object-fit:cover;object-fit:cover}.e-con-inner>.elementor-widget-video,.e-con>.elementor-widget-video{width:var(--container-widget-width);--flex-grow:var(--container-widget-flex-grow)}<\/style>\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2de10f3b elementor-widget elementor-widget-text-editor\" data-id=\"2de10f3b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>But to make the MLOps shift a success, it is essential to break down the silos between operations and data teams, and to ensure that the latter have the necessary resources (Data Engineers, Machine Learning Engineers). Machine Learning Engineers are experienced profiles with expertise in machine learning (statistical modeling, deep learning, etc.), software development, data engineering and the production roll-out of ML models.<\/p><p>Interested in becoming a MLOps? Find out more about our MLOps training course!<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-13d5b6c8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"13d5b6c8\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-37dbda\" data-id=\"37dbda\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7544285a elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"7544285a\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/ml-ops-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover our MLOps Course<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>In a previous article, we presented the DevOps philosophy, and how this new approach enables faster value delivery for businesses through the unification of development (Dev) and operations (Ops) teams, which previously worked in silos. In this article, we&#8217;ll look at the application of this approach to Machine Learning problems: we&#8217;re talking about MLOps. Back [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":172013,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2409],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>MLOps: DevOps applied to Machine Learning projects<\/title>\n<meta name=\"description\" content=\"Explore the realm of MLOps, where the principles of DevOps converge with Machine Learning. Learn how MLOps streamlines, automates, and enhances the...\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"MLOps: DevOps applied to Machine Learning projects\" \/>\n<meta property=\"og:description\" content=\"In a previous article, we presented the DevOps philosophy, and how this new approach enables faster value delivery for businesses through the unification\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects\" \/>\n<meta property=\"og:site_name\" content=\"Data Science Courses | DataScientest\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataScientestEN\" \/>\n<meta property=\"article:published_time\" content=\"2023-10-15T13:00:43+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-10-15T13:05:52+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/en\/files\/2023\/10\/mlops.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"562\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Melanie\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:site\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Melanie\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimated reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects#article\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects\"},\"author\":{\"name\":\"Melanie\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d\"},\"headline\":\"MLOps: DevOps applied to Machine Learning projects\",\"datePublished\":\"2023-10-15T13:00:43+00:00\",\"dateModified\":\"2023-10-15T13:05:52+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects\"},\"wordCount\":853,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/datascientest.com\/en\/#organization\"},\"articleSection\":[\"Machine Learning\"],\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects#respond\"]}],\"copyrightYear\":\"2023\",\"copyrightHolder\":{\"@id\":\"https:\/\/datascientest.com\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects\",\"url\":\"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects\",\"name\":\"MLOps: DevOps applied to Machine Learning projects\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/en\/#website\"},\"datePublished\":\"2023-10-15T13:00:43+00:00\",\"dateModified\":\"2023-10-15T13:05:52+00:00\",\"description\":\"Explore the realm of MLOps, where the principles of DevOps converge with Machine Learning. Learn how MLOps streamlines, automates, and enhances the...\",\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/en\/#website\",\"url\":\"https:\/\/datascientest.com\/en\/\",\"name\":\"Data Science Courses | DataScientest\",\"description\":\"Leader in Data Science Courses\",\"publisher\":{\"@id\":\"https:\/\/datascientest.com\/en\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/en\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-GB\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/datascientest.com\/en\/#organization\",\"name\":\"DataScientest\",\"url\":\"https:\/\/datascientest.com\/en\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png\",\"contentUrl\":\"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png\",\"width\":301,\"height\":301,\"caption\":\"DataScientest\"},\"image\":{\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/DataScientestEN\",\"https:\/\/twitter.com\/DataScientest__\",\"https:\/\/www.linkedin.com\/school\/datascientest\/mycompany\/\",\"https:\/\/www.tiktok.com\/@datascientest\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d\",\"name\":\"Melanie\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g\",\"caption\":\"Melanie\"},\"url\":\"https:\/\/datascientest.com\/en\/author\/melaniekrause\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"MLOps: DevOps applied to Machine Learning projects","description":"Explore the realm of MLOps, where the principles of DevOps converge with Machine Learning. Learn how MLOps streamlines, automates, and enhances the...","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects","og_locale":"en_GB","og_type":"article","og_title":"MLOps: DevOps applied to Machine Learning projects","og_description":"In a previous article, we presented the DevOps philosophy, and how this new approach enables faster value delivery for businesses through the unification","og_url":"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects","og_site_name":"Data Science Courses | DataScientest","article_publisher":"https:\/\/www.facebook.com\/DataScientestEN","article_published_time":"2023-10-15T13:00:43+00:00","article_modified_time":"2023-10-15T13:05:52+00:00","og_image":[{"width":1024,"height":562,"url":"https:\/\/datascientest.com\/en\/files\/2023\/10\/mlops.png","type":"image\/png"}],"author":"Melanie","twitter_card":"summary_large_image","twitter_creator":"@DataScientest__","twitter_site":"@DataScientest__","twitter_misc":{"Written by":"Melanie","Estimated reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects#article","isPartOf":{"@id":"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects"},"author":{"name":"Melanie","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d"},"headline":"MLOps: DevOps applied to Machine Learning projects","datePublished":"2023-10-15T13:00:43+00:00","dateModified":"2023-10-15T13:05:52+00:00","mainEntityOfPage":{"@id":"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects"},"wordCount":853,"commentCount":0,"publisher":{"@id":"https:\/\/datascientest.com\/en\/#organization"},"articleSection":["Machine Learning"],"inLanguage":"en-GB","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects#respond"]}],"copyrightYear":"2023","copyrightHolder":{"@id":"https:\/\/datascientest.com\/#organization"}},{"@type":"WebPage","@id":"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects","url":"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects","name":"MLOps: DevOps applied to Machine Learning projects","isPartOf":{"@id":"https:\/\/datascientest.com\/en\/#website"},"datePublished":"2023-10-15T13:00:43+00:00","dateModified":"2023-10-15T13:05:52+00:00","description":"Explore the realm of MLOps, where the principles of DevOps converge with Machine Learning. Learn how MLOps streamlines, automates, and enhances the...","inLanguage":"en-GB","potentialAction":[{"@type":"ReadAction","target":["https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects"]}]},{"@type":"WebSite","@id":"https:\/\/datascientest.com\/en\/#website","url":"https:\/\/datascientest.com\/en\/","name":"Data Science Courses | DataScientest","description":"Leader in Data Science Courses","publisher":{"@id":"https:\/\/datascientest.com\/en\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/datascientest.com\/en\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-GB"},{"@type":"Organization","@id":"https:\/\/datascientest.com\/en\/#organization","name":"DataScientest","url":"https:\/\/datascientest.com\/en\/","logo":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/","url":"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png","contentUrl":"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png","width":301,"height":301,"caption":"DataScientest"},"image":{"@id":"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/DataScientestEN","https:\/\/twitter.com\/DataScientest__","https:\/\/www.linkedin.com\/school\/datascientest\/mycompany\/","https:\/\/www.tiktok.com\/@datascientest"]},{"@type":"Person","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d","name":"Melanie","image":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g","caption":"Melanie"},"url":"https:\/\/datascientest.com\/en\/author\/melaniekrause"}]}},"_links":{"self":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts\/172011"}],"collection":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/users\/67"}],"replies":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/comments?post=172011"}],"version-history":[{"count":0,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts\/172011\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/media\/172013"}],"wp:attachment":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/media?parent=172011"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/categories?post=172011"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/tags?post=172011"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}