{"id":7094,"date":"2025-09-26T12:18:44","date_gmt":"2025-09-26T12:18:44","guid":{"rendered":"https:\/\/www.talentelgia.com\/blog\/?p=7094"},"modified":"2025-09-26T12:18:46","modified_gmt":"2025-09-26T12:18:46","slug":"what-is-mlops","status":"publish","type":"post","link":"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/","title":{"rendered":"MLops: Lifecycle, Implementation &#038; DevOps Comparison"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_73 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#What_is_MLops\" title=\"What is MLops?\">What is MLops?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#Why_Do_You_Need_MLOps\" title=\"Why Do You Need MLOps?\">Why Do You Need MLOps?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#Benefits_Of_MLOps\" title=\"Benefits Of MLOps\">Benefits Of MLOps<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#How_to_Implement_MLOps\" title=\"How to Implement MLOps?&nbsp;\">How to Implement MLOps?&nbsp;<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#1_MLOps_Level_0\" title=\"1. MLOps Level 0\">1. MLOps Level 0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#2_MLOps_Level_1\" title=\"2. MLOps Level 1\">2. MLOps Level 1<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#3_MLOps_Level_2\" title=\"3. MLOps Level 2\">3. MLOps Level 2<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#MLops_Lifecycle\" title=\"MLops Lifecycle&nbsp;\">MLops Lifecycle&nbsp;<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#1_Define_the_Problem\" title=\"1. Define the Problem\">1. Define the Problem<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#2_Data_Collection_and_Preparation\" title=\"2. Data Collection and Preparation\">2. Data Collection and Preparation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#3_Model_Development\" title=\"3. &nbsp; Model Development\">3. &nbsp; Model Development<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#4_Model_Monitoring_and_Maintenance\" title=\"4. Model Monitoring and Maintenance\">4. Model Monitoring and Maintenance<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#Difference_Between_MLOps_Vs_DevOps\" title=\"Difference Between MLOps Vs DevOps\">Difference Between MLOps Vs DevOps<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#Conclusion\" title=\"Conclusion\">Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n\n<p>One of the greatest hurdles companies face today is bringing a machine learning model from development to production. It\u2019s the models that work well in a lab environment but then falter when exposed to real-world data, changing business requirements, or the limitations of the infrastructure. Filling this gap requires more than simply constructing accurate models; it necessitates a disciplined methodology that integrates data science, engineering, and operations. In this post, we\u2019re going to talk about what MLOps is, the components of MLOps, and how to implement MLops. And the difference between MLops and DevOps. Let\u2019s get started:&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_is_MLops\"><\/span><strong>What is MLops?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>MLOps (or Machine Learning Operations) are a series of practices that aim to simplify the deployment, monitoring, and management of machine learning models in production. It fills the gap between data science and operations teams, making sure that ML models run as expected in production and systems can be continuously improved.<\/p>\n\n\n\n<p>Born from <strong><a href=\"https:\/\/www.talentelgia.com\/blog\/devops-in-software-development\/\" target=\"_blank\" rel=\"noreferrer noopener\">DevOps in Software Development<\/a><\/strong>, MLOps focuses on overcoming the operationalized AI and machine learning challenges. Data scientists are responsible for building and training models, and engineers take charge of deploying and maintaining them. MLOps facilitates a collaborative workflow, which allows enterprises to deploy ML models quickly, above and beyond keeping performance in control and tangible value flowing in real applications.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Why_Do_You_Need_MLOps\"><\/span><strong>Why Do You Need MLOps?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>MLOps isn\u2019t just a technical process; it\u2019s the foundation for scaling <a href=\"https:\/\/www.talentelgia.com\/solutions\/ai-business-solutions\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>AI Business Solutions<\/strong><\/a> effectively. By streamlining how machine learning models are developed, deployed, and maintained, MLOps helps businesses unlock new revenue streams, reduce operational costs, and enhance decision-making with data-driven insights. From automating workflows to improving customer experience, MLOps bridges the gap between AI research and real-world enterprise adoption.<\/p>\n\n\n\n<p>Without this framework, achieving consistent results is difficult. Automating model pipelines accelerates time-to-market while keeping expenses under control, allowing teams to stay agile in rapidly changing industries. Whether it\u2019s handling sensitive data, working with limited budgets, or managing small teams, MLOps provides the flexibility to adapt practices to fit your unique business environment.<\/p>\n\n\n\n<p>The real power of MLOps lies in its adaptability; it\u2019s not a one-size-fits-all solution. Companies can experiment, refine, and retain only the practices that align with their strategic goals. By bridging the gap between the <a href=\"https:\/\/www.talentelgia.com\/blog\/ai-in-devops\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>role of AI in DevOps<\/strong><\/a> and machine learning operations, MLOps becomes more than a process; it evolves into a growth enabler for businesses that want to stay competitive in the AI-driven era.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Benefits_Of_MLOps\"><\/span><strong>Benefits Of MLOps<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Think of MLOps as the \u201cproject manager\u201d for your AI initiatives. Without it, machine learning models risk staying stuck in the lab, full of potential but unable to scale. With it, businesses can bring structure, speed, and strategy to their AI journey. Whether you\u2019re building complex web development projects, launching innovative app development ideas, or integrating cutting-edge AI integration services, MLOps ensures your efforts deliver consistent, measurable results.<\/p>\n\n\n\n<p><strong>1. Faster deployments<br><\/strong> By streamlining workflows through automation and continuous testing, MLOps ensures that ML models can move from experimentation to production rapidly. This agility helps companies launch smarter apps, enhance web platforms, and update AI-driven features with faster time-to-market.<\/p>\n\n\n\n<p><strong>2. Improved collaboration.<br><\/strong> MLOps bridges the gap between data scientists, developers, and IT teams. Just like in agile web or app development projects, breaking down silos fosters smoother communication and reduces bottlenecks. The result: end-to-end collaboration from data preparation to deployment and monitoring.<\/p>\n\n\n\n<p><strong>3. Higher-quality models.<br><\/strong> When supported by an MLOps framework, <strong>AI models<\/strong> are not only more accurate but also adaptive to evolving datasets. Businesses that pair MLOps with AI integration services can build more reliable and intelligent products, whether it\u2019s a recommendation system in an app or advanced analytics in a web platform.<\/p>\n\n\n\n<p><strong>4. Enhanced efficiency.<br><\/strong> Automation removes repetitive, time-consuming tasks, freeing up experts to focus on high-value problem-solving and innovation. This mirrors the productivity gains seen in web and app development when workflows are optimized, ensuring businesses spend more time creating impact and less time managing manual tasks.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_to_Implement_MLOps\"><\/span><strong>How to Implement MLOps?&nbsp;<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>MLOps products are important for companies that want to tighten up their machine learning operations and make the most out of <strong><a href=\"https:\/\/www.talentelgia.com\/blog\/ai-model-architecture\/\" target=\"_blank\" rel=\"noreferrer noopener\">AI models<\/a><\/strong>. Machine Learning Operations (MLOps), also known as AI\/ML Ops, enable teams to bring control and efficiency to the full lifecycle of ML models, from development and deployment to monitoring and management. The MLOps deployment has been split into three groups, based on the level of automation and maturity. Here\u2019s a step-by-step breakdown.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1000\" height=\"465\" src=\"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/internal2-mlops-1.webp\" alt=\"How to Implement MLOps? \" class=\"wp-image-7100\" srcset=\"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/internal2-mlops-1.webp 1000w, https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/internal2-mlops-1-300x140.webp 300w, https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/internal2-mlops-1-768x357.webp 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_MLOps_Level_0\"><\/span><strong>1. MLOps Level 0<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>MLOps Level 0 is the initial stage of Machine Learning Operations (MLOps) for your organization if you are getting started with AI\/ML Ops. There is no automation at this tier in the machine learning pipeline. Every part, including data preparation, model training, testing, or deployment, is manually managed by data scientists.<\/p>\n\n\n\n<p><strong>Key Features:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Manual Workflow: <\/strong>All the steps (preprocessing, training, and validation) are manually performed by data scientists through scripts and notebooks.<\/li>\n\n\n\n<li><strong>Decoupling of Responsibilities: <\/strong>Data scientists write models, engineers deploy them (as a prediction service and as an API).<\/li>\n\n\n\n<li><strong>Rare Refreshment:<\/strong> Models are updated or retrained seldomly.<\/li>\n\n\n\n<li><strong>No CI\/CD \u2013<\/strong> Continuous integration and deployment is not as common.<\/li>\n\n\n\n<li><strong>Scant Monitoring: <\/strong>Model performance monitoring is scarce, and API deployment may include some security or load testing; prediction monitoring tends to be an afterthought.<\/li>\n<\/ul>\n\n\n\n<p><strong>Challenges:<\/strong><strong><br><\/strong>Manual machine learning operations MLOps struggle to handle real-world changes in data. Implementing CI\/CD and continuous testing improves reliability and performance.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_MLOps_Level_1\"><\/span><strong>2. MLOps Level 1<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Level 1 MLOps consists of automation of the ML pipeline to have continuous model training and real-time prediction serving.<\/p>\n\n\n\n<p><strong>Key Features:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Automated Experiments: <\/strong>ML experiment is coordinated on automation, and no interference from human parameters.<\/li>\n\n\n\n<li><strong>No need to retrain models:<\/strong> continuous training with the latest data ensures predictions are current.<\/li>\n\n\n\n<li><strong>Pipeline Consistency: <\/strong>The consistent pipeline from development through preproduction to production environments helps stable machine learning op use.<\/li>\n\n\n\n<li><strong>Reusable Components: <\/strong>Pipelines and components can be reused and are Docker containerized for flexibility.<\/li>\n\n\n\n<li><strong>Online Model Delivery:<\/strong> Models as prediction services are serving immediately after they are trained.<\/li>\n\n\n\n<li><strong>Automated Pipeline Deployment:<\/strong> Full training pipeline runs periodically and updates models to production easily.<\/li>\n<\/ul>\n\n\n\n<p><strong>Additional Components:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Data &amp; Model Validation:<\/strong> Automated checks ensure both incoming data and models meet quality standards.<\/li>\n\n\n\n<li><strong>Feature Repository:<\/strong> Centralized storage for features used in training and serving ensures consistency.<\/li>\n\n\n\n<li><strong>Metadata Management:<\/strong> Tracks pipeline executions for reproducibility, debugging, and audit purposes.<\/li>\n\n\n\n<li><strong>Pipeline Activation:<\/strong> Models retrain automatically based on triggers like new data, scheduled intervals, or performance drops.<\/li>\n<\/ul>\n\n\n\n<p><strong>Challenges:<\/strong><\/p>\n\n\n\n<p>Level 1 is fine for dynamic environments, but if you want to play with new ML concepts, a better CI\/CD pipeline is needed to test and deploy code quickly.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_MLOps_Level_2\"><\/span><strong>3. MLOps Level 2<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Level 2 MLOps is the most advanced maturity, with fully automated CI\/CD pipelines in place to enable fast and frequent model updates and experimentation. This is perfect for AI\/ML Ops at a massive scale in production.<\/p>\n\n\n\n<p><strong>Key Features:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Experimentation &amp; Iterative Development:<\/strong> New ML algorithms, feature engineering, and hyperparameters are creatively tested iteratively under most cases in repositories to track source code changes.<\/li>\n\n\n\n<li><strong>Pipeline CI: <\/strong>Automated build, test, and package of pipeline artifacts help to ensure deployments are consistent.<\/li>\n\n\n\n<li><strong>Pipeline CD:<\/strong> released packages are deployed to production automatically in a CI\/CD fashion that can crowdsource from the federation any model that meets acceptance.<\/li>\n\n\n\n<li><strong>Automated triggers:<\/strong> Pipelines retrain and deploy models according to schedule, new data, or model performance deterioration.<\/li>\n\n\n\n<li><strong>Persistent Model Provision: <\/strong>Trained models are directly available as prediction utilities.<\/li>\n\n\n\n<li><strong>Monitoring &amp; Feedback:<\/strong> We now monitor in real time, and when necessary, we trigger a new pipeline cycle or experiment.<\/li>\n<\/ul>\n\n\n\n<p><strong>Challenges:<\/strong><\/p>\n\n\n\n<p>Level 2 is agile, resource-efficient, and meticulous work. Infrastructure can be strained by frequent retraining, while feature store and metadata management is complicated. Automatic data analysis, model validation, and pipeline triggering are necessary for a stable model.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"MLops_Lifecycle\"><\/span><strong>MLops Lifecycle&nbsp;<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Implementing machine learning at scale requires more than just building a model\u2014it demands a structured approach that connects data, development, deployment, and ongoing optimization. <\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1000\" height=\"345\" src=\"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/internal1-mlops-1.webp\" alt=\"MLOps Lifecycle\" class=\"wp-image-7099\" srcset=\"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/internal1-mlops-1.webp 1000w, https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/internal1-mlops-1-300x104.webp 300w, https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/internal1-mlops-1-768x265.webp 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n\n\n\n<p>The MLOps lifecycle provides this framework, ensuring reliability, scalability, and alignment with business goals. Below are the key stages every organization should follow:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_Define_the_Problem\"><\/span><strong>1. Define the Problem<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p><strong><br><\/strong>The initial step in the MLOps lifecycle is to define the problem that your ML model requires solving. This includes:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Establishing clear goals for the project.<\/li>\n\n\n\n<li>Establishing target KPI\u2019s to measure success.<\/li>\n<\/ul>\n\n\n\n<p>A clear problem description is key to aligning all subsequent steps with the business context and KPIs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_Data_Collection_and_Preparation\"><\/span><strong>2. Data Collection and Preparation<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>High-quality data is the backbone of successful AI\/ML Ops. In this stage, teams:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Collect relevant data from multiple sources.<\/li>\n\n\n\n<li>Clean, filter, and transform the data to ensure consistency.<\/li>\n\n\n\n<li>Prepare it for model training by handling missing values, normalizing features, and creating training\/testing datasets.<\/li>\n<\/ul>\n\n\n\n<p>Proper data preparation is crucial, as the accuracy and efficiency of the model directly depend on the data quality.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_Model_Development\"><\/span><strong>3. &nbsp; Model Development<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>After data becomes available, data scientists can work on building machine learning models that involve:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Selecting the right algorithms.<\/li>\n\n\n\n<li>Tuning hyperparameters for optimal performance.<\/li>\n\n\n\n<li>Evaluating the model on scalar metrics (accuracy, precision, recall or F1-score).<\/li>\n<\/ul>\n\n\n\n<p>This is an iterative Stage, and models are repeatedly improved until they reach acceptable levels of performance.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_Model_Monitoring_and_Maintenance\"><\/span><strong>4. Model Monitoring and Maintenance<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Deployment is not the final step. Continuous monitoring ensures your model performs reliably under real-world conditions:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Track model performance using KPIs and real-time metrics.<br><\/li>\n\n\n\n<li>Detect issues like model drift or declining accuracy.<br><\/li>\n\n\n\n<li>Update, retrain, or fine-tune the model as needed to maintain high performance.<br><\/li>\n<\/ul>\n\n\n\n<p>This stage ensures that machine learning operations remain robust, scalable, and adaptable to changing business requirements.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Difference_Between_MLOps_Vs_DevOps\"><\/span><strong>Difference Between MLOps Vs DevOps<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Although DevOps and MLOps (Machine Learning Operations) share the same objective of simplifying processes for efficacy improvement, their emphasis is totally different. DevOps mainly caters to software\/application development; on the other hand, MLOps are designed considering machine learning models and data-driven systems. It&#8217;s important to know the differing business needs when choosing a process so that we can simulate ways in which they fit against these goals.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Aspect<\/strong><\/td><td><strong>DevOps<\/strong><\/td><td><strong>MLOps<\/strong><\/td><\/tr><tr><td><strong>Primary Goal<\/strong><\/td><td>To accelerate software delivery through automation, collaboration, and continuous integration\/deployment (CI\/CD).<\/td><td>To streamline the end-to-end ML lifecycle \u2014 from data preparation and model training to deployment and monitoring.<\/td><\/tr><tr><td><strong>Core Focus<\/strong><\/td><td>Focused mainly on code, application performance, and infrastructure.<\/td><td>Focused mainly on data, model accuracy, and adaptability.<\/td><\/tr><tr><td><strong>Iteration Cycle<\/strong><\/td><td>Iterations are short and frequent (daily\/weekly) for deploying software updates.<\/td><td>Iterations depend on data changes \u2014 retraining happens when new data, drift, or performance issues arise.<\/td><\/tr><tr><td><strong>Tools &amp; Frameworks<\/strong><\/td><td>Uses CI\/CD tools like Jenkins, GitHub Actions, Docker, and Kubernetes.<\/td><td>Uses ML-specific tools like Kubeflow, MLflow, TFX, SageMaker, and MLOps AWS services.<\/td><\/tr><tr><td><strong>Team Involvement<\/strong><\/td><td>Involves developers, testers, and operations engineers.<\/td><td>Involves data scientists, ML engineers, and DevOps engineers.<\/td><\/tr><tr><td><strong>Scalability Needs<\/strong><\/td><td>Focuses on scaling application servers, APIs, and infrastructure for end-users.<\/td><td>Focuses on scaling data pipelines, ML training, and model deployments.<\/td><\/tr><tr><td><strong>Testing Approach<\/strong><\/td><td>Software is tested through unit tests, integration tests, and regression tests.<\/td><td>ML models are tested through data validation, bias detection, model accuracy, drift detection, and A\/B testing.<\/td><\/tr><tr><td><strong>Deployment Style<\/strong><\/td><td>Code is deployed as applications or microservices.<\/td><td>Models are deployed as prediction services, APIs, or batch inference jobs.<\/td><\/tr><tr><td><strong>Version Control<\/strong><\/td><td>Manages code versions through Git and CI\/CD systems.<\/td><td>Manages data versions, model versions, and experiment tracking.<\/td><\/tr><tr><td><strong>Failure Management<\/strong><\/td><td>Rollbacks or patches are applied when code fails.<\/td><td>Requires retraining or fine-tuning when models fail due to drift, bias, or poor accuracy.<\/td><\/tr><tr><td><strong>Monitoring Metrics<\/strong><\/td><td>Monitors system uptime, latency, and errors.<\/td><td>Monitors model accuracy, precision, recall, F1-score, and data drift.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<pre class=\"wp-block-verse\">MLOps (Machine Learning Operations) is not a nice-to-have, but a must-have for organizations that want to derive significant business benefits from their AI\/ML initiatives. By taking the seams out between data science, engineering, and IT operations, MLOps ensures ML models elegantly transition from experiments to production, where they are scalable, reliable, and regulatory compliant. MLOps has more challenging requirements than DevOps: it's not enough in MLOps to just deliver code, but you'll also need to monitor your models, track data drift, and run retraining cycles. <br>In the context of businesses, embracing MLOps is more than just about making AI\/ML Ops pipelines efficient \u2013 it\u2019s about unlocking sustainable value from machine learning in day-to-day operations.<\/pre>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>One of the greatest hurdles companies face today is bringing a machine learning model from development to production. It\u2019s the models that work well in a lab environment but then falter when exposed to real-world data, changing business requirements, or the limitations of the infrastructure. Filling this gap requires more than simply constructing accurate models; [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":7098,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[186],"tags":[],"class_list":["post-7094","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-devops"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>MLops : Lifecycle, Implementation &amp; DevOps Comparison<\/title>\n<meta name=\"description\" content=\"MLOps explained: Discover its lifecycle, practical implementation, and a clear comparison with DevOps for smarter ML operations.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"MLops : Lifecycle, Implementation &amp; DevOps Comparison\" \/>\n<meta property=\"og:description\" content=\"MLOps explained: Discover its lifecycle, practical implementation, and a clear comparison with DevOps for smarter ML operations.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/\" \/>\n<meta property=\"og:site_name\" content=\"Talentelgia\" \/>\n<meta property=\"article:published_time\" content=\"2025-09-26T12:18:44+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-09-26T12:18:46+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/MLOps-Feature.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"1080\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Advait Upadhyay\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Advait Upadhyay\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"9 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/\"},\"author\":{\"name\":\"Advait Upadhyay\",\"@id\":\"https:\/\/www.talentelgia.com\/blog\/#\/schema\/person\/6db713566abc30413982d157f2262bbc\"},\"headline\":\"MLops: Lifecycle, Implementation &#038; DevOps Comparison\",\"datePublished\":\"2025-09-26T12:18:44+00:00\",\"dateModified\":\"2025-09-26T12:18:46+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/\"},\"wordCount\":1911,\"publisher\":{\"@id\":\"https:\/\/www.talentelgia.com\/blog\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/MLOps-Feature.webp\",\"articleSection\":[\"DevOps\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/\",\"url\":\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/\",\"name\":\"MLops : Lifecycle, Implementation & DevOps Comparison\",\"isPartOf\":{\"@id\":\"https:\/\/www.talentelgia.com\/blog\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/MLOps-Feature.webp\",\"datePublished\":\"2025-09-26T12:18:44+00:00\",\"dateModified\":\"2025-09-26T12:18:46+00:00\",\"description\":\"MLOps explained: Discover its lifecycle, practical implementation, and a clear comparison with DevOps for smarter ML operations.\",\"breadcrumb\":{\"@id\":\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#primaryimage\",\"url\":\"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/MLOps-Feature.webp\",\"contentUrl\":\"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/MLOps-Feature.webp\",\"width\":1920,\"height\":1080,\"caption\":\"What is MLOps: Lifecycle, Implementation & DevOps Comparison\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.talentelgia.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"MLops: Lifecycle, Implementation &#038; DevOps Comparison\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.talentelgia.com\/blog\/#website\",\"url\":\"https:\/\/www.talentelgia.com\/blog\/\",\"name\":\"Talentelgia\",\"description\":\"Latest Web &amp; Mobile Technologies, AI\/ML, and Blockchain Blogs\",\"publisher\":{\"@id\":\"https:\/\/www.talentelgia.com\/blog\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.talentelgia.com\/blog\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.talentelgia.com\/blog\/#organization\",\"name\":\"Talentelgia\",\"url\":\"https:\/\/www.talentelgia.com\/blog\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.talentelgia.com\/blog\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2024\/01\/talentelgia-logo.svg\",\"contentUrl\":\"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2024\/01\/talentelgia-logo.svg\",\"width\":159,\"height\":53,\"caption\":\"Talentelgia\"},\"image\":{\"@id\":\"https:\/\/www.talentelgia.com\/blog\/#\/schema\/logo\/image\/\"}},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.talentelgia.com\/blog\/#\/schema\/person\/6db713566abc30413982d157f2262bbc\",\"name\":\"Advait Upadhyay\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.talentelgia.com\/blog\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2024\/09\/advait-sir.webp\",\"contentUrl\":\"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2024\/09\/advait-sir.webp\",\"caption\":\"Advait Upadhyay\"},\"description\":\"Advait Upadhyay is a well-experienced IT professional with over 15 years of industry know-how. He is the co-founder of Talentelgia Technologies and has a real passion for tech, eagerly following the cutting edge of new tech products and discoveries, of which he is always ready to express in his blog. The main purpose of his approach is to show business owners and organizations how to develop custom IT solutions that are suitable for their particular business cases. Advait's focus on innovation is not just about motivating his team but also about positioning Talentelgia as a market-dominant provider of services like AI\/ML, web, app, and blockchain development. Advait is not only leading his company, but he also becomes an exemplar in the technology industry. He is the pioneer who is breaking the way to a new world.\",\"sameAs\":[\"https:\/\/www.talentelgia.com\/\",\"https:\/\/www.linkedin.com\/company\/talentelgia-technologies\",\"https:\/\/www.linkedin.com\/in\/advaitupadhyay\/\"],\"url\":\"https:\/\/www.talentelgia.com\/blog\/author\/admin\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"MLops : Lifecycle, Implementation & DevOps Comparison","description":"MLOps explained: Discover its lifecycle, practical implementation, and a clear comparison with DevOps for smarter ML operations.","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:\/\/www.talentelgia.com\/blog\/what-is-mlops\/","og_locale":"en_US","og_type":"article","og_title":"MLops : Lifecycle, Implementation & DevOps Comparison","og_description":"MLOps explained: Discover its lifecycle, practical implementation, and a clear comparison with DevOps for smarter ML operations.","og_url":"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/","og_site_name":"Talentelgia","article_published_time":"2025-09-26T12:18:44+00:00","article_modified_time":"2025-09-26T12:18:46+00:00","og_image":[{"width":1920,"height":1080,"url":"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/MLOps-Feature.webp","type":"image\/webp"}],"author":"Advait Upadhyay","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Advait Upadhyay","Est. reading time":"9 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#article","isPartOf":{"@id":"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/"},"author":{"name":"Advait Upadhyay","@id":"https:\/\/www.talentelgia.com\/blog\/#\/schema\/person\/6db713566abc30413982d157f2262bbc"},"headline":"MLops: Lifecycle, Implementation &#038; DevOps Comparison","datePublished":"2025-09-26T12:18:44+00:00","dateModified":"2025-09-26T12:18:46+00:00","mainEntityOfPage":{"@id":"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/"},"wordCount":1911,"publisher":{"@id":"https:\/\/www.talentelgia.com\/blog\/#organization"},"image":{"@id":"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#primaryimage"},"thumbnailUrl":"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/MLOps-Feature.webp","articleSection":["DevOps"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/","url":"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/","name":"MLops : Lifecycle, Implementation & DevOps Comparison","isPartOf":{"@id":"https:\/\/www.talentelgia.com\/blog\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#primaryimage"},"image":{"@id":"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#primaryimage"},"thumbnailUrl":"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/MLOps-Feature.webp","datePublished":"2025-09-26T12:18:44+00:00","dateModified":"2025-09-26T12:18:46+00:00","description":"MLOps explained: Discover its lifecycle, practical implementation, and a clear comparison with DevOps for smarter ML operations.","breadcrumb":{"@id":"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#primaryimage","url":"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/MLOps-Feature.webp","contentUrl":"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2025\/09\/MLOps-Feature.webp","width":1920,"height":1080,"caption":"What is MLOps: Lifecycle, Implementation & DevOps Comparison"},{"@type":"BreadcrumbList","@id":"https:\/\/www.talentelgia.com\/blog\/what-is-mlops\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.talentelgia.com\/blog\/"},{"@type":"ListItem","position":2,"name":"MLops: Lifecycle, Implementation &#038; DevOps Comparison"}]},{"@type":"WebSite","@id":"https:\/\/www.talentelgia.com\/blog\/#website","url":"https:\/\/www.talentelgia.com\/blog\/","name":"Talentelgia","description":"Latest Web &amp; Mobile Technologies, AI\/ML, and Blockchain Blogs","publisher":{"@id":"https:\/\/www.talentelgia.com\/blog\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.talentelgia.com\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.talentelgia.com\/blog\/#organization","name":"Talentelgia","url":"https:\/\/www.talentelgia.com\/blog\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.talentelgia.com\/blog\/#\/schema\/logo\/image\/","url":"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2024\/01\/talentelgia-logo.svg","contentUrl":"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2024\/01\/talentelgia-logo.svg","width":159,"height":53,"caption":"Talentelgia"},"image":{"@id":"https:\/\/www.talentelgia.com\/blog\/#\/schema\/logo\/image\/"}},{"@type":"Person","@id":"https:\/\/www.talentelgia.com\/blog\/#\/schema\/person\/6db713566abc30413982d157f2262bbc","name":"Advait Upadhyay","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.talentelgia.com\/blog\/#\/schema\/person\/image\/","url":"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2024\/09\/advait-sir.webp","contentUrl":"https:\/\/www.talentelgia.com\/blog\/wp-content\/uploads\/2024\/09\/advait-sir.webp","caption":"Advait Upadhyay"},"description":"Advait Upadhyay is a well-experienced IT professional with over 15 years of industry know-how. He is the co-founder of Talentelgia Technologies and has a real passion for tech, eagerly following the cutting edge of new tech products and discoveries, of which he is always ready to express in his blog. The main purpose of his approach is to show business owners and organizations how to develop custom IT solutions that are suitable for their particular business cases. Advait's focus on innovation is not just about motivating his team but also about positioning Talentelgia as a market-dominant provider of services like AI\/ML, web, app, and blockchain development. Advait is not only leading his company, but he also becomes an exemplar in the technology industry. He is the pioneer who is breaking the way to a new world.","sameAs":["https:\/\/www.talentelgia.com\/","https:\/\/www.linkedin.com\/company\/talentelgia-technologies","https:\/\/www.linkedin.com\/in\/advaitupadhyay\/"],"url":"https:\/\/www.talentelgia.com\/blog\/author\/admin\/"}]}},"_links":{"self":[{"href":"https:\/\/www.talentelgia.com\/blog\/wp-json\/wp\/v2\/posts\/7094","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.talentelgia.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.talentelgia.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.talentelgia.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.talentelgia.com\/blog\/wp-json\/wp\/v2\/comments?post=7094"}],"version-history":[{"count":5,"href":"https:\/\/www.talentelgia.com\/blog\/wp-json\/wp\/v2\/posts\/7094\/revisions"}],"predecessor-version":[{"id":7104,"href":"https:\/\/www.talentelgia.com\/blog\/wp-json\/wp\/v2\/posts\/7094\/revisions\/7104"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.talentelgia.com\/blog\/wp-json\/wp\/v2\/media\/7098"}],"wp:attachment":[{"href":"https:\/\/www.talentelgia.com\/blog\/wp-json\/wp\/v2\/media?parent=7094"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.talentelgia.com\/blog\/wp-json\/wp\/v2\/categories?post=7094"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.talentelgia.com\/blog\/wp-json\/wp\/v2\/tags?post=7094"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}