{"id":95435,"date":"2024-08-14T12:12:58","date_gmt":"2024-08-14T09:12:58","guid":{"rendered":"https:\/\/www.instinctools.com\/?p=95435"},"modified":"2025-07-15T10:56:08","modified_gmt":"2025-07-15T07:56:08","slug":"mlops-vs-devops","status":"publish","type":"post","link":"https:\/\/www.instinctools.com\/blog\/mlops-vs-devops\/","title":{"rendered":"MLOps vs DevOps: Evolution in Operational Excellence"},"content":{"rendered":"\n<div class=\"wp-block-yoast-seo-table-of-contents yoast-table-of-contents\"><h2>Contents<\/h2><ul><li><a href=\"#h-what-is-mlops-looking-at-the-concept-through-the-devops-lenses\" data-level=\"2\">What is MLOps? Looking at the concept through the DevOps lenses<\/a><\/li><li><a href=\"#h-devops-walked-so-mlops-can-run-how-mlops-wins-adopters-minds\" data-level=\"2\">DevOps walked so MLOps can run. How MLOps wins adopters\u2019 minds<\/a><\/li><li><a href=\"#h-comparing-mlops-and-devops-workflows\" data-level=\"2\">Comparing MLOps and DevOps workflows<\/a><\/li><li><a href=\"#h-adoption-drivers-for-mlops-are-the-same-for-devops-or-are-they\" data-level=\"2\">Adoption drivers for MLOps are the same for DevOps. Or are they?<\/a><\/li><li><a href=\"#h-it-s-not-devops-vs-mlops-it-s-devops-and-mlops\" data-level=\"2\">It\u2019s not DevOps vs MLOps, it\u2019s DevOps and MLOps<\/a><\/li><\/ul><\/div>\n\n\n\n<p>Being good enough once doesn\u2019t guarantee being good enough forever. This evolutionary idea also works for the DevOps methodology, which has long been the gold standard for streamlining software development and deployment, emphasizing collaboration. It was enough to tackle the challenges of the previous decade but hit the wall as machine learning (ML) gained momentum, bringing new challenges.&nbsp;<\/p>\n\n\n\n<p>The good news is that the methodology can be fine-tuned to keep up with advancing technologies. Imagine the same iterative approach, continuous integration, continuous delivery, continuous training, and continuous monitoring, but applied to ML models. Enter <a href=\"\/mlops-consulting-services\/\" target=\"_blank\" rel=\"noreferrer noopener\">MLOps<\/a>, designed to match the specifics of working within the machine learning domain.<\/p>\n\n\n\n<p>The two approaches have a lot of overlaps, as you\u2019ll see in our side-by-side MLOps vs. DevOps comparison.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-is-mlops-looking-at-the-concept-through-the-devops-lenses\">What is MLOps? Looking at the concept through the DevOps lenses<\/h2>\n\n\n\n<p>MLOps, short for machine learning operations, builds upon the foundational principles of DevOps, sharing the same goal of automating processes. But instead of optimizing software development and delivery pipeline, as DevOps does, MLOps extends beyond deploying code. It strives to <strong>automate and standardize processes across the entire ML lifecycle<\/strong>, allowing organizations to operationalize AI at scale.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/www.instinctools.com\/wp-content\/uploads\/2024\/08\/mlops-vs-devops_-evolution-in-operational-excellence_02-1024x683.png\" alt=\"Flowchart showing how MLOps works\" class=\"wp-image-95437\"\/><\/figure>\n\n\n\n<p>From data pipelines to model training and infrastructure management, MLOps tools align ML application development (Dev) with ML system deployment and operations (Ops), maximizing the value of your machine learning investments.&nbsp;<\/p>\n\n\n\n<p>Note: In the trio of AIOps vs MLOps vs DevOps, AIOps and MLOps also have different priorities. While MLOps is an extension of DevOps tailored to the machine learning domain, AIOps leverages AI and ML to automate IT operations.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-devops-walked-so-mlops-can-run-how-mlops-wins-adopters-minds\">DevOps walked so MLOps can run. How MLOps wins adopters\u2019 minds<\/h2>\n\n\n\n<p>According to McKinsey, adopters of comprehensive MLOps practices shelve <a href=\"https:\/\/www.mckinsey.com\/capabilities\/quantumblack\/our-insights\/scaling-ai-like-a-tech-native-the-ceos-role\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">30 percent<\/a> fewer models and squeeze 60% more value out of their AI initiatives. These figures come off as hardly surprising, considering DevOps-inspired improvements machine learning operations usher into the AI development workflows.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-automation-and-continuous-processes\">Automation and continuous processes<\/h3>\n\n\n\n<p>A hands-free approach to managing the software development life cycle is the bedrock of DevOps, powered by the implementation of automation tools that minimize human effort. Software development and operations also hinge on iterative, ongoing activities, all aimed at accelerating software delivery without reducing its reliability:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Continuous integration (CI),<\/li>\n\n\n\n<li>Continuous delivery (CD),<\/li>\n\n\n\n<li>Continuous deployment.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>MLOps takes these principles up a notch, laying them over the entire ML lifecycle. For example, <strong>automated model training kicks into action right after model training code updates or data changes<\/strong>, while automated testing allows AI teams to <strong>flag issues early in development<\/strong> and stop them in their tracks. Just like DevOps, MLOps adheres to an iterative approach where models are constantly monitored, evaluated, and refined through continuous integration, continuous delivery, continuous training, and continuous monitoring.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-version-control-systems-and-configuration-management\">Version control systems and configuration management<\/h3>\n\n\n\n<p>Effective versioning of code and configuration changes are among other tenets that carry DevOps and enable development teams to collaborate effectively, perform experiment tracking, and manage code.&nbsp;<\/p>\n\n\n\n<p>In the same vein, <strong>MLOps applies version control to datasets, model code, and configurations<\/strong>, ensuring reproducibility, auditability, and consistency across artificial intelligence development flows.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-automated-monitoring-and-feedback-loops\">Automated monitoring and feedback loops<\/h3>\n\n\n\n<p>DevOps is all about keeping a pulse on applications in production in case any issues come up, plus continuous monitoring helps track user interactions.&nbsp;<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>In MLOps, continuous monitoring manifests in a slightly different way and <strong>applies mainly to model performance, data quality, and infrastructure health<\/strong>. Through feedback loops, AI and data science teams keep model performance in check and effortlessly spot issues like data drift, concept drift, or performance degradation.<\/p>\n<cite>\u2014 Pavel Klapatsiuk, AI Lead Engineer, *instinctools<\/cite><\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-testing-and-validation\">Testing and validation<\/h3>\n\n\n\n<p>In DevOps, comprehensive testing and validation strategies revolve around automated testing, continuous integration, and continuous delivery (CI\/CD) pipelines. This approach also entails a variety of testing scenarios, including unit testing, integration testing, performance testing, and other checks, integrated early in the development.<\/p>\n\n\n\n<p>Here, MLOps follows suit, but extends testing to comprise tests for features and data, tests for model development, and tests for ML infrastructure. This holistic approach <strong>reduces the risk of deployment failures<\/strong> and allows engineers to <a href=\"https:\/\/www.instinctools.com\/blog\/ai-model-optimization\/\" target=\"_blank\" rel=\"noreferrer noopener\">tackle unique challenges<\/a> like model drift and insufficient explainability.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-infrastructure-scalability-and-flexibility\">Infrastructure scalability and flexibility<\/h3>\n\n\n\n<p>By adopting such DevOps practices as infrastructure-as-code, continuous delivery, microservices architecture, and others, companies can adapt to fluctuating workloads and rapid changes in application requirements.<\/p>\n\n\n\n<p>Given the complexity of models and overwhelming data volumes, <strong>scalability also takes the central stage<\/strong> in MLOps. Along with scalability-friendly DevOps tools and practices, MLOps also employs containerization and orchestration to enable hassle-free application deployment and scaling across different environments.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-collaboration\">Collaboration<\/h3>\n\n\n\n<p>At-scale automation and optimization of development workflows becomes a pipe dream without bridging the gaps between development and operations teams. That\u2019s why a sync between the two is a central theme in DevOps.<\/p>\n\n\n\n<p>MLOps expands the collaborative circle to <strong>include data scientists, ML engineers, and IT operations<\/strong>, streamlining the transition of machine learning models from development to production environments. Moreover, MLOps-inspired collaboration relies on model lifecycle management and governance.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>Now you might think that DevOps alone can address the challenges brought about by ML workflows. But in reality, it falls short of providing a holistic ecosystem. <strong>With DevOps alone, all sides of the development process work in silos<\/strong>, dealing with unpredictable model experiments and manual model release processes. DevOps doesn\u2019t imply result traceability or reproducibility so without MLOps, AI teams can\u2019t deliver reliable, trustworthy, and compliant machine learning models.<\/p>\n<cite>\u2014 Pavel Klapatsiuk, AI Lead Engineer, *instinctools<\/cite><\/blockquote>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/www.instinctools.com\/wp-content\/uploads\/2024\/08\/mlops-vs-devops_-evolution-in-operational-excellence_03-1024x683.png\" alt=\"DevOps without MLOps for ML workflows\" class=\"wp-image-95438\"\/><\/figure>\n\n\n\n<div class=\"wp-block-cta-blog-block-cta cta-blog\"><span class=\"draw draw_color-right draw_undefined\"><\/span><span class=\"draw draw_color-left draw_gray\"><\/span><div class=\"cta-blog__wrap\"><div class=\"cta-blog__left\" style=\"max-width:367px\"><p class=\"cta-blog__title\">DevOps or MLOps, we can do it both<\/p><p class=\"cta-blog__desc\"><\/p><\/div><div class=\"button button_undefined button_bg-gray cta-blog__btn\"><a href=\"#contact-form\" class=\"link-anchor\" target=\"_self\" rel=\"noopener\">Reach out to our team<\/a><\/div><\/div><div class=\"cta-blog__form form_light\"><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-comparing-mlops-and-devops-workflows\">Comparing MLOps and DevOps workflows<\/h2>\n\n\n\n<p>Both DevOps and MLOps lifecycles circulate around automated deployment, quality control, and continuous feedback \u2014 with a common goal of automating and streamlining processes. However, the paths to value are different for each approach.&nbsp;<\/p>\n\n\n\n<p><strong>DevOps starts as early as the development environment set-up and segues into the coding stage which is then followed by the CI stage and automated testing<\/strong>. This lifecycle stretches as far as <strong>post-deployment monitoring<\/strong>, allowing development teams to enhance incident response and implement continuous improvement.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/www.instinctools.com\/wp-content\/uploads\/2024\/08\/mlops-vs-devops_-evolution-in-operational-excellence_04-1024x683.png\" alt=\"DevOps lifecycle\" class=\"wp-image-95442\"\/><\/figure>\n\n\n\n<p>While the DevOps lifecycle consists of 8 steps, <strong>the MLOps one comprises five core stages, directly tied to model development, deployment, and management<\/strong>. The lifecycle unfolds with data preparation, supported by versioning, pipeline building, and data labeling. This step flows smoothly into model development and training, encompassing experiment tracking, training automation, and model versioning.&nbsp;&nbsp;<\/p>\n\n\n\n<p>MLOps also provides a structured approach to model deployment and post-deployment optimization, providing such automated capabilities as containerization, autoscaling, and more.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/www.instinctools.com\/wp-content\/uploads\/2024\/08\/mlops-vs-devops_-evolution-in-operational-excellence_05-1024x683.png\" alt=\"MLOps lifecycle\" class=\"wp-image-95439\"\/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-adoption-drivers-for-mlops-are-the-same-for-devops-or-are-they\">Adoption drivers for MLOps are the same for DevOps. Or are they?<\/h2>\n\n\n\n<p>While the rationale for introducing MLOps and DevOps practices may vary across organizations, common adoption drivers overlap. Both methodologies do a great job of reducing development cycle times, enhancing productivity, promoting system reliability, and last \u2014 but far from the least \u2014 fostering healthier collaborative practices. Here is a rundown of <strong>benefits that MLOps has brought to our clients\u2019 table<\/strong>:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-1-accelerated-scalability-across-business-processes-workflows-and-customer-journeys\">1. Accelerated scalability across business processes, workflows, and customer journeys<\/h3>\n\n\n\n<p>Executives often lament that the transition from AI solution idea to implementation can stretch up to over a year \u2014 and the progress remains sluggish, no matter how hefty their investments are. MLOps flips the script, allowing AI adopters to go from zero to hero in about <a href=\"https:\/\/www.mckinsey.com\/capabilities\/quantumblack\/our-insights\/scaling-ai-like-a-tech-native-the-ceos-role\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">2 to 12 weeks<\/a> with no additional talent or technical debt.&nbsp;<\/p>\n\n\n\n<p>This momentum stems from MLOps-induced standardization that is achieved through the creation of reusable components and workflow automation. Erstwhile time- and effort-consuming tasks like data ingestion, data management, and data integration become an easy lift, with little need for human oversight.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>Modular pre-made components can lay the ground for creating a larger product or system \u2014 something that helped our client, a fintech company, deploy the solution five times faster and with fewer resources. By developing a central AI platform and layering modular pre-made components on top, they rapidly adapted their recommendation engine for different countries, improving customers\u2019 access to relevant financial products and investments.<\/p>\n<cite>\u2014 Pavel Klapatsiuk, AI Lead Engineer, *instinctools<\/cite><\/blockquote>\n\n\n\n<p>Along with reusable model training scripts and deployment infrastructure, MLOps allows organizations to diversify their portfolio of reusable assets to include ready-to-use data products. These artifacts consolidate a particular set of data according to common standards, facilitating its repurposing for multiple current and future use cases within a specific field.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-2-enhanced-data-acquisition-and-preprocessing\">2. Enhanced data acquisition and preprocessing<\/h3>\n\n\n\n<p>Traditional, manual-based workflows are known for time-consuming data workflows due to inconsistent data formats, data silos, and difficulties in tracking data changes. From data loading to data transformation, automated data pipelines \u2014 aimed at extracting, transforming, and loading data efficiently \u2014 <strong>relieve data professionals of manual tasks<\/strong>.&nbsp;<\/p>\n\n\n\n<p>Today, data pipelines can also be augmented by an <a href=\"\/blog\/llm-use-cases\/\" target=\"_blank\" rel=\"noreferrer noopener\">LLM<\/a>, besides other traditional MLOps tools, to automate data processing activities, such as data cleaning, anomaly detection, and data summarization.<\/p>\n\n\n\n<p>Beyond maintaining data quality, data pipelines reduce errors, facilitate governance, and, of course, <strong>reduce the time it takes to collect and process data<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-3-easier-dataset-classification-and-management\">3. Easier dataset classification and management<\/h3>\n\n\n\n<p>Whether your data engineers are enhancing data security, ensuring compliance, or buckling up for an <a href=\"https:\/\/www.instinctools.com\/data-analytics-services\/\" target=\"_blank\" rel=\"noreferrer noopener\">advanced data analytics<\/a> solution, data classification is an essential building block for effective data management and protection.\u00a0<\/p>\n\n\n\n<p>MLOps takes the inefficiency out of data classification and management by providing robust metadata management systems that group and tag datasets based on their source, type, and quality. Unified data repositories further facilitate consistent dataset management, making classification and timely access less of a struggle.<\/p>\n\n\n\n<p>Moreover, MLOps processes seamlessly integrate with existing data governance frameworks, aligning data classification with the organizational policies of your company.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-4-closer-insight-into-the-effectiveness-of-dataset-changes\">4. Closer insight into the effectiveness of dataset changes<\/h3>\n\n\n\n<p>In <a href=\"\/machine-learning-app-development-services\/\" target=\"_blank\" rel=\"noreferrer noopener\">ML development<\/a>, comparing different datasets allows data engineering teams to:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>keep tabs on the performance of a machine learning model,<\/li>\n\n\n\n<li>spot data issues,<\/li>\n\n\n\n<li>improve model interpretability.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>MLOps versioning tools facilitate tracking and managing changes made to various components of an ML solution over time.<\/p>\n\n\n\n<p>This deep dive comparison is accompanied by experiment tracking \u2014 the process of jotting down all experiment-related information alongside model performance metrics and hyperparameters \u2014 to reveal patterns in the interplay of different experiments. Once AI teams have singled out two models with the highest accuracy, they can run A\/B testing, which is another MLOps gismo for comparing model performance with different dataset versions.<\/p>\n\n\n\n<p>Moreover, MLOps tools provide a venue for organized experimentation, allowing development teams to easily reproduce previous runs, compare different models or configurations, and recreate experiments for verification or debugging.&nbsp;&nbsp;<\/p>\n\n\n\n<p>Finally, built-in analytics and visualization tools enable engineers to keep a detailed lab notebook for how changes in datasets impact model accuracy, precision, recall, and other performance metrics.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-5-ensured-regulatory-compliance-at-scale\">5. Ensured regulatory compliance at scale<\/h3>\n\n\n\n<p>While a holistic risk management strategy is non-negotiable for all machine learning projects, the practical implementation of risk management strategies hinges on the practices used by <a href=\"https:\/\/www.instinctools.com\/machine-learning-consulting-services\/\" target=\"_blank\" rel=\"noreferrer noopener\">AI teams<\/a>. MLOps kits out teams with ample tools for comprehensive model governance, including metadata tracking, centralized repositories, model versioning, and other trackers.&nbsp;<\/p>\n\n\n\n<p>Reusable elements, equipped with detailed documentation on their structure, use, and risk considerations, also reduce error rates and allow for seamless, uniform component updates to filter down to dependent AI solutions.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>One of our fintech clients who operates in a domain with a long tradition of strict regulations contacted our MLOps team to increase the auditability of their deployed models. By implementing CI\/CD integration, metadata management, and pipeline orchestration, we empowered their team to maintain a comprehensive audit trail of model changes and associated compliance considerations.<\/p>\n<cite>\u2014 Pavel Klapatsiuk, AI Lead Engineer, *instinctools<\/cite><\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-6-improved-model-quality-at-lower-costs\">6. Improved model quality at lower costs<\/h3>\n\n\n\n<p>Automation in itself is a powerful tool for cost-friendly quality improvement, but there\u2019s a lot more to the cost-saving potential of machine learning operations. Thanks to continuous monitoring of model performance, MLOps tools detect issues early in development and trigger automated retraining, refining model quality over time with no overhead costs. Also, by continuously monitoring and reporting resource usage, MLOps tools reveal cost-saving opportunities.<\/p>\n\n\n\n<p>Moreover, MLOps aligns resource allocation with actual needs, reducing idle times and costs and enabling dynamic scaling, which is essential for handling traffic spikes without compromising performance or incurring additional expenses.&nbsp;&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-it-s-not-devops-vs-mlops-it-s-devops-and-mlops\">It\u2019s not DevOps vs MLOps, it\u2019s DevOps and MLOps<\/h2>\n\n\n\n<p>While DevOps focuses on accelerating software delivery and reliability, MLOps is more about improving ML model deployment and management. But despite seemingly different objectives, these methodologies do not cancel each other out. Instead, MLOps draws upon core DevOps principles, transcending the software development field and covering the unique needs and challenges of the ML development lifecycle.&nbsp;<\/p>\n\n\n\n<p>For this very reason, pure-play DevOps, although setting up a strong foundation for software development, is not enough for environments with data as a first-class citizen, making MLOps a backbone for all things machine learning.<\/p>\n\n\n\n<div class=\"wp-block-cta-blog-block-cta cta-blog\"><span class=\"draw draw_color-right draw_undefined\"><\/span><span class=\"draw draw_color-left draw_gray\"><\/span><div class=\"cta-blog__wrap\"><div class=\"cta-blog__left\" style=\"max-width:367px\"><p class=\"cta-blog__title\">Looking for an MLOps partner to join your AI project?<\/p><p class=\"cta-blog__desc\"><\/p><\/div><div class=\"button button_undefined button_bg-gray cta-blog__btn\"><a href=\"#contact-form\" class=\"link-anchor\" target=\"_self\" rel=\"noopener\">Let\u2019s pool our efforts<\/a><\/div><\/div><div class=\"cta-blog__form form_light\"><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Discover a fresh take on MLOps vs DevOps, including their similarities, differences, and core adoption drivers.<\/p>\n","protected":false},"author":29,"featured_media":95446,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"cta":"","footnotes":""},"categories":[715,361],"products_posts":[],"consulting_posts":[714],"industry_posts":[],"engagement_model_posts":[],"class_list":["post-95435","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-development","category-devops-services","consulting_posts-machine-learning-consulting"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v24.5 (Yoast SEO v24.5) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>MLOps vs DevOps: Head to Head Comparison 2025 | 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