{"id":4241,"date":"2022-10-24T13:01:29","date_gmt":"2022-10-24T21:01:29","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?page_id=4241"},"modified":"2025-05-29T13:47:52","modified_gmt":"2025-05-29T13:47:52","slug":"model-monitoring-missing-piece-to-your-mlops-puzzle","status":"publish","type":"page","link":"https:\/\/www.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/","title":{"rendered":"Model Monitoring: The Missing Piece to Your MLOps Puzzle"},"content":{"rendered":"\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-group alignwide is-layout-constrained wp-block-group-is-layout-constrained\" style=\"margin-top:var(--wp--preset--spacing--100);margin-bottom:var(--wp--preset--spacing--50)\">\n<h1 class=\"wp-block-heading has-text-align-center has-accent-color has-text-color has-body-s-font-size\" style=\"text-transform:uppercase\">Machine Learning Operations<\/h1>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\" style=\"margin-top:var(--wp--preset--spacing--40);margin-bottom:var(--wp--preset--spacing--40)\">Model Monitoring: The Missing Piece to Your MLOps Puzzle<\/h2>\n\n\n\n<p class=\"has-text-align-center has-body-l-font-size\">Here we share a comprehensive guide to model monitoring in production.<\/p>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading has-display-s-font-size\" style=\"margin-top:var(--wp--preset--spacing--100)\">Table of Contents<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"#mlops-cycle\">The MLOps Lifecycle<\/a><\/li>\n\n\n\n<li><a href=\"#deployment-not-final\">Deployment Is Not the Final Step: Here\u2019s Why<\/a><\/li>\n\n\n\n<li><a href=\"#challenges\">Challenges in Monitoring the ML Life Cycle<\/a><\/li>\n\n\n\n<li><a href=\"#why-you-need-monitoring\">Why You Need Monitoring<\/a><\/li>\n\n\n\n<li><a href=\"#monitoring-vs-obersavibility\">Monitoring vs. Observability: What\u2019s the Difference?<\/a><\/li>\n\n\n\n<li><a href=\"#best-practices\">Best Practices in Monitoring ML Models in Production<\/a><\/li>\n\n\n\n<li><a href=\"#going-beyond-monitoring\">It Doesn\u2019t Stop Here: Going Beyond Monitoring<\/a><\/li>\n\n\n\n<li><a href=\"#faqs\">FAQs<\/a><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"border-radius:5px;margin-top:var(--wp--preset--spacing--50)\">Introduction: Will this guide be helpful to me?<\/h2>\n\n\n\n<p><strong>This guide will be helpful to you if you are:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Learn more about model monitoring in machine learning.<\/li>\n\n\n\n<li>Discover best practices in model monitoring to assist you in your existing or future ML projects.<\/li>\n\n\n\n<li>Optimize your ML workflow using the information that we provide in this article.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"mlops-cycle\" style=\"border-radius:5px;margin-top:var(--wp--preset--spacing--50)\">The MLOps Lifecycle<\/h2>\n\n\n\n<p>MLOps is a set of management techniques for the deep learning or production ML lifecycle, formed from machine learning or ML and operations or Ops. These include ML and DevOps methods, as well as data engineering procedures meant to effectively and reliably install and maintain ML models in production. MLOps promotes communication and cooperation between operations experts and data scientists to accomplish successful machine learning model lifecycle management.<\/p>\n\n\n\n<p>The MLOps lifecycle consists of:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Model building<\/li>\n\n\n\n<li>Model evaluation and experimentation<\/li>\n\n\n\n<li>Productionizing model<\/li>\n\n\n\n<li>Testing<\/li>\n\n\n\n<li>Deployment<\/li>\n\n\n\n<li>Monitoring and observability<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1008\" height=\"1024\" src=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image2-3-1008x1024.png\" alt=\"mlops lifecycle steps in bubbles\" class=\"wp-image-4245\" style=\"width:720px\" srcset=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image2-3-1008x1024.png 1008w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image2-3-295x300.png 295w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image2-3-768x780.png 768w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image2-3.png 1080w\" sizes=\"auto, (max-width: 1008px) 100vw, 1008px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"deployment-not-final\" style=\"border-radius:5px;margin-top:var(--wp--preset--spacing--50)\">Don\u2019t Stop at Deployment. Here\u2019s Why.<\/h2>\n\n\n\n<p>The cycle does not end once the model has been trained, tested, and deployed. We must guarantee that the deployed model works in the long run and keep an eye out for any issues.<\/p>\n\n\n\n<p>After the deployment phase, you should ensure the continuous delivery and data feedback loop.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"challenges\" style=\"border-radius:5px;margin-top:var(--wp--preset--spacing--50)\">Challenges in Monitoring the ML Lifecycle<\/h2>\n\n\n\n<p>ML workflow is divided into several stages, which we\u2019ll review in detail below.<\/p>\n\n\n\n<p>But why should you keep track of your models?<\/p>\n\n\n\n<p>To address this question, consider some of the production challenges your model may face:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1. Data Distribution Changes<\/h3>\n\n\n\n<p>Key questions: Why are the values of my features suddenly changing?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2. Model Ownership<\/h3>\n\n\n\n<p>Key questions: Who owns the production model? The DevOps team? Data scientists? Engineers?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3. Training-Serving Skew<\/h3>\n\n\n\n<p>Key questions: Why, despite our intensive testing and validation efforts throughout development, is the model producing poor outcomes in production?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4. Model or Concept Drift<\/h3>\n\n\n\n<p>Key questions: Why was my model doing well in production before abruptly deteriorating over time?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">5. Black Box Models<\/h3>\n\n\n\n<p>Key questions: How can I evaluate and communicate my model\u2019s predictions to important stakeholders per the business objective?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">6. Concerted Adversaries<\/h3>\n\n\n\n<p>Key questions: How can I secure my model\u2019s safety? Is my model under attack?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">7. Model Readiness<\/h3>\n\n\n\n<p>Key questions: How will I compare findings from a newer version(s) of my model to those from the current version(s)?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">8. Pipeline Health Issues<\/h3>\n\n\n\n<p>Key questions: Why is my training pipeline failing to execute? Why does it take so long to complete a retraining job?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">9. Data Quality Issues<\/h3>\n\n\n\n<p>Key questions: Why is my training pipeline failing to execute? Why does it take so long to complete a retraining job?<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">10. Underperforming System<\/h3>\n\n\n\n<p>Key questions: Why is my predictive service latency so high? Why am I receiving such a wide range of latencies for my different models?<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"why-you-need-monitoring\" style=\"border-radius:5px;margin-top:var(--wp--preset--spacing--50)\">Why You Need Model Monitoring in ML<\/h2>\n\n\n\n<p>There are several reasons to monitor machine learning models. It allows you to assess prediction accuracy, reduce prediction mistakes, and fine-tune models for optimal performance.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"860\" src=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image1-3-1024x860.png\" alt=\"monitoring ml in a bubble with 3 steps around it\" class=\"wp-image-4244\" style=\"width:520px\" srcset=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image1-3-1024x860.png 1024w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image1-3-300x252.png 300w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image1-3-768x645.png 768w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image1-3.png 1080w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Eliminate Poor Generalization<\/h3>\n\n\n\n<p>A machine learning model is often trained on a restricted portion of the total in-domain data due to a lack of labeled data or other computational restrictions. Even though the approach is designed to eliminate bias, the practice results in poor generalization. As a result, the sample of output data will be wrong or inefficient. This problem can be resolved by using monitoring models. It enables you to build models that are balanced and precise without overfitting or underfitting the data.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Eliminate the Issue of Changing Parameters Over Time<\/h3>\n\n\n\n<p>The variables and parameters at a certain period are used to optimize a model. By the time the model is deployed, the same parameters will be irrelevant. A sentiment model constructed 5 years ago, for example, may incorrectly categorize the emotion of particular words or phrases. As a result, the forecast will be inaccurate. Model monitoring helps you to resolve the issue by analyzing how a model performs on real-world data over time.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Ensure the Stability of Prediction<\/h3>\n\n\n\n<p>The machine learning model\u2019s input isn\u2019t independent. As a result, modifications in any aspect of the system, including hyper-parameters and sampling methods, might result in unexpected results. Model monitoring guarantees that predictions are very stable by measuring several stability measures such as the Population Stability Index (PSI) and Characteristic Stability Index (CSI).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"monitoring-vs-obersavibility\" style=\"border-radius:5px;margin-top:var(--wp--preset--spacing--50)\">Model Monitoring vs. Observability: What\u2019s the Difference<\/h2>\n\n\n\n<p>One commonly asked question is, \u201cI already monitor my data.\u201d \u201cWhy do I require observability as well?\u201dThat\u2019s an excellent question. Monitoring and observability have long been used interchangeably, although they are not the same thing.<\/p>\n\n\n\n<p>Data observability enables monitoring, which most technical practitioners are familiar with: we want to be the first to know when anything fails and to solve it as soon as possible. Data quality monitoring functions similarly, notifying teams when a data asset appears to be different from what the specified measurements or parameters indicate.<\/p>\n\n\n\n<p>Data monitoring, for example, might provide an alert if a number fell outside of an expected range, data was not updated as planned, or 100 million rows suddenly became 1 million. However, before you can monitor a data ecosystem, you must have insight into all of the properties we\u2019ve just discussed \u2014 this is where data observability comes in.<\/p>\n\n\n\n<p>Data observability also facilitates active learning by giving granular, in-context data insights. Teams can investigate data assets, analyze schema modifications, and pinpoint the source of new or unforeseen problems. Monitoring, on the other hand, generates alerts based on pre-defined concerns and represents data in aggregates and averages.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"best-practices\" style=\"border-radius:5px;margin-top:var(--wp--preset--spacing--50)\">Best Practices in Monitoring ML Models in Production<\/h2>\n\n\n\n<p>You should keep the following points in mind to ensure the success of your machine learning model in real life:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1. Data Distribution Shifts<\/h3>\n\n\n\n<p>Over time, model performance might decline due to data drift. Monitoring the inputs to your model can allow you to detect these drifts swiftly. When a data drift happens, it is best practice to then re-train the model on the data it wasn\u2019t performing well on to improve generalization.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2. Performance Shifts<\/h3>\n\n\n\n<p>Model monitoring allows you to track changes in performance. As a consequence, you can assess the model\u2019s performance. It also teaches you how to efficiently debug if something goes wrong.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3. Data Integrity<\/h3>\n\n\n\n<p>The dependability of data throughout its lifespan is referred to as data integrity. You must check that the information is correct. There are other approaches, including error checking and validation.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"going-beyond-monitoring\" style=\"border-radius:5px;margin-top:var(--wp--preset--spacing--50)\">It Doesn\u2019t Stop Here: Going Beyond Monitoring<\/h2>\n\n\n\n<p>Continuously improving your models does not end with ML monitoring; delve deeper to genuinely understand your models with ML Observability, which includes ML monitoring, validation, and troubleshooting to improve model performance and boost AI ROI. ML Observability enables your teams to automatically discover model flaws, diagnose difficult-to-find errors, and enhance your models\u2019 performance in production.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"faqs\" style=\"border-radius:5px;margin-top:var(--wp--preset--spacing--50)\">Frequently Asked Questions (FAQs)<\/h2>\n\n\n\n<div class=\"wp-block-comet-accordion-accordion comet-accordion\">\n<details class=\"wp-block-comet-accordion-item comet-accordion__item\" open><summary class=\"comet-accordion__item-summary\"><span>What are the best tools to use for machine learning model monitoring?<\/span><span class=\"comet-accordion__item-icon\" aria-hidden=\"true\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"16\" height=\"16\" fill=\"none\" stroke=\"#191A1C\"><path stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"2\" d=\"M8 1v14m7-7H1\"><\/path><\/svg><\/span><\/summary><div class=\"comet-accordion__item-content\">\n<p>The best tools for ML model monitoring are <a href=\"https:\/\/www.anodot.com\">Anodot<\/a>, <a href=\"https:\/\/www.fiddler.ai\">Fiddler<\/a>, and<a href=\"https:\/\/cloud.google.com\/products\/ai\"> Google Cloud AI Platform<\/a>.<\/p>\n<\/div><\/details>\n\n\n\n<details class=\"wp-block-comet-accordion-item comet-accordion__item\"><summary class=\"comet-accordion__item-summary\"><span>Why is documentation important in ML model monitoring?<\/span><span class=\"comet-accordion__item-icon\" aria-hidden=\"true\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"16\" height=\"16\" fill=\"none\" stroke=\"#191A1C\"><path stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"2\" d=\"M8 1v14m7-7H1\"><\/path><\/svg><\/span><\/summary><div class=\"comet-accordion__item-content\">\n<p>Documentation is important in ML model monitoring as it guarantees that the model has enough computational resources to handle inference workloads.<\/p>\n<\/div><\/details>\n\n\n\n<details class=\"wp-block-comet-accordion-item comet-accordion__item\"><summary class=\"comet-accordion__item-summary\"><span>What\u2019s the difference between functional vs. operational model monitoring?<\/span><span class=\"comet-accordion__item-icon\" aria-hidden=\"true\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"16\" height=\"16\" fill=\"none\" stroke=\"#191A1C\"><path stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"2\" d=\"M8 1v14m7-7H1\"><\/path><\/svg><\/span><\/summary><div class=\"comet-accordion__item-content\">\n<p>You can monitor what might go wrong with your machine learning model in production at two different levels:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Functional level monitoring \u2013 entails keeping tabs on model performance, inputs (data), and outputs (predictions).<\/li>\n\n\n\n<li>Operational level monitoring \u2013 refers to monitoring at the system and resource levels.<\/li>\n<\/ul>\n<\/div><\/details>\n\n\n\n<details class=\"wp-block-comet-accordion-item comet-accordion__item\"><summary class=\"comet-accordion__item-summary\"><span>What are the best metrics to use to monitor models in production?<\/span><span class=\"comet-accordion__item-icon\" aria-hidden=\"true\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"16\" height=\"16\" fill=\"none\" stroke=\"#191A1C\"><path stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"2\" d=\"M8 1v14m7-7H1\"><\/path><\/svg><\/span><\/summary><div class=\"comet-accordion__item-content\">\n<p>The most optimal model metric to utilize is determined mostly by the type of model and the distribution of the data it is predicting.<\/p>\n<\/div><\/details>\n\n\n\n<details class=\"wp-block-comet-accordion-item comet-accordion__item\"><summary class=\"comet-accordion__item-summary\"><span>Do I need dedicated and expert annotators in the model monitoring stage?<\/span><span class=\"comet-accordion__item-icon\" aria-hidden=\"true\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"16\" height=\"16\" fill=\"none\" stroke=\"#191A1C\"><path stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"2\" d=\"M8 1v14m7-7H1\"><\/path><\/svg><\/span><\/summary><div class=\"comet-accordion__item-content\">\n<p>While dedicated and expert annotators can help in the model monitoring stage, you don\u2019t need to have them.<\/p>\n<\/div><\/details>\n\n\n\n<details class=\"wp-block-comet-accordion-item comet-accordion__item\"><summary class=\"comet-accordion__item-summary\"><span>How can model monitoring help the organization reduce costs?<\/span><span class=\"comet-accordion__item-icon\" aria-hidden=\"true\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"16\" height=\"16\" fill=\"none\" stroke=\"#191A1C\"><path stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"2\" d=\"M8 1v14m7-7H1\"><\/path><\/svg><\/span><\/summary><div class=\"comet-accordion__item-content\">\n<p>The rising expenses of audits and compliance reviews are putting pressure on organizations to develop a cost-effective and long-term method of confirming control performance. The monitoring stage in MLOps automates internal controls testing across the enterprise\u2019s major financial and operational operations.<\/p>\n<\/div><\/details>\n<\/div>\n\n\n\n<div style=\"height:var(--wp--preset--spacing--50)\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Table of Contents Introduction: Will this guide be helpful to me? This guide will be helpful to you if you are: The MLOps Lifecycle MLOps is a set of management techniques for the deep learning or production ML lifecycle, formed from machine learning or ML and operations or Ops. These include ML and DevOps methods, [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":4776,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"customer_name":"","customer_description":"","customer_industry":"","customer_technologies":"","customer_logo":"","footnotes":""},"coauthors":[108],"class_list":["post-4241","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Model Monitoring: The Missing Piece to Your MLOps Puzzle<\/title>\n<meta name=\"description\" content=\"Why shouldn\u2019t you stop at model deployment and why is model monitoring important? Read this comprehensive guide to monitoring your models.\" \/>\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.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Model Monitoring: The Missing Piece to Your MLOps Puzzle\" \/>\n<meta property=\"og:description\" content=\"Why shouldn\u2019t you stop at model deployment and why is model monitoring important? Read this comprehensive guide to monitoring your models.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/\" \/>\n<meta property=\"og:site_name\" content=\"Comet\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/cometdotml\" \/>\n<meta property=\"article:modified_time\" content=\"2025-05-29T13:47:52+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image2-3-1008x1024.png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@Cometml\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"9 minutes\" \/>\n\t<meta name=\"twitter:label2\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data2\" content=\"Sharmila Chockalingam\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Model Monitoring: The Missing Piece to Your MLOps Puzzle","description":"Why shouldn\u2019t you stop at model deployment and why is model monitoring important? Read this comprehensive guide to monitoring your models.","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.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/","og_locale":"en_US","og_type":"article","og_title":"Model Monitoring: The Missing Piece to Your MLOps Puzzle","og_description":"Why shouldn\u2019t you stop at model deployment and why is model monitoring important? Read this comprehensive guide to monitoring your models.","og_url":"https:\/\/www.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/","og_site_name":"Comet","article_publisher":"https:\/\/www.facebook.com\/cometdotml","article_modified_time":"2025-05-29T13:47:52+00:00","og_image":[{"url":"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image2-3-1008x1024.png","type":"","width":"","height":""}],"twitter_card":"summary_large_image","twitter_site":"@Cometml","twitter_misc":{"Est. reading time":"9 minutes","Written by":"Sharmila Chockalingam"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/#article","isPartOf":{"@id":"https:\/\/www.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/"},"author":{"name":"engineering@atre.net","@id":"https:\/\/www.comet.com\/site\/#\/schema\/person\/550ac35e8e821db8064c5bd1f0a04e6b"},"headline":"Model Monitoring: The Missing Piece to Your MLOps Puzzle","datePublished":"2022-10-24T21:01:29+00:00","dateModified":"2025-05-29T13:47:52+00:00","mainEntityOfPage":{"@id":"https:\/\/www.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/"},"wordCount":1407,"publisher":{"@id":"https:\/\/www.comet.com\/site\/#organization"},"image":{"@id":"https:\/\/www.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/#primaryimage"},"thumbnailUrl":"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image2-3-1008x1024.png","inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/www.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/","url":"https:\/\/www.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/","name":"Model Monitoring: The Missing Piece to Your MLOps Puzzle","isPartOf":{"@id":"https:\/\/www.comet.com\/site\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/#primaryimage"},"image":{"@id":"https:\/\/www.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/#primaryimage"},"thumbnailUrl":"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image2-3-1008x1024.png","datePublished":"2022-10-24T21:01:29+00:00","dateModified":"2025-05-29T13:47:52+00:00","description":"Why shouldn\u2019t you stop at model deployment and why is model monitoring important? Read this comprehensive guide to monitoring your models.","breadcrumb":{"@id":"https:\/\/www.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/#primaryimage","url":"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image2-3.png","contentUrl":"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/10\/image2-3.png","width":1080,"height":1097,"caption":"mlops lifecycle steps in bubbles"},{"@type":"BreadcrumbList","@id":"https:\/\/www.comet.com\/site\/lp\/model-monitoring-missing-piece-to-your-mlops-puzzle\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.comet.com\/site\/"},{"@type":"ListItem","position":2,"name":"LP","item":"https:\/\/www.comet.com\/site\/lp\/"},{"@type":"ListItem","position":3,"name":"Model Monitoring: The Missing Piece to Your MLOps Puzzle"}]},{"@type":"WebSite","@id":"https:\/\/www.comet.com\/site\/#website","url":"https:\/\/www.comet.com\/site\/","name":"Comet","description":"Build Better Models Faster","publisher":{"@id":"https:\/\/www.comet.com\/site\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.comet.com\/site\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.comet.com\/site\/#organization","name":"Comet ML, Inc.","alternateName":"Comet","url":"https:\/\/www.comet.com\/site\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.comet.com\/site\/#\/schema\/logo\/image\/","url":"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2025\/01\/logo_comet_square.png","contentUrl":"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2025\/01\/logo_comet_square.png","width":310,"height":310,"caption":"Comet ML, Inc."},"image":{"@id":"https:\/\/www.comet.com\/site\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/cometdotml","https:\/\/x.com\/Cometml","https:\/\/www.youtube.com\/channel\/UCmN63HKvfXSCS-UwVwmK8Hw"]},{"@type":"Person","@id":"https:\/\/www.comet.com\/site\/#\/schema\/person\/550ac35e8e821db8064c5bd1f0a04e6b","name":"engineering@atre.net","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.comet.com\/site\/#\/schema\/person\/image\/027c18177377edf459980f0cfb83706c","url":"https:\/\/secure.gravatar.com\/avatar\/d002a459a297e0d1779329318029aee19868c312b3e1f3c9ec9b3e3add2740de?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/d002a459a297e0d1779329318029aee19868c312b3e1f3c9ec9b3e3add2740de?s=96&d=mm&r=g","caption":"engineering@atre.net"},"sameAs":["https:\/\/live-cometml.pantheonsite.io"],"url":"https:\/\/www.comet.com\/site\/blog\/author\/engineeringatre-net\/"}]}},"_links":{"self":[{"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/pages\/4241","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/comments?post=4241"}],"version-history":[{"count":3,"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/pages\/4241\/revisions"}],"predecessor-version":[{"id":16112,"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/pages\/4241\/revisions\/16112"}],"up":[{"embeddable":true,"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/pages\/4776"}],"wp:attachment":[{"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/media?parent=4241"}],"wp:term":[{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/coauthors?post=4241"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}