{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Kurtis Pykes",
  "title_listed": "Model Drift in Machine Learning",
  "keywords": "模型漂移,监控",
  "domain": "机器学习",
  "content_type": "深度技术解析",
  "is_paid_flag": "部分免费/会员墙",
  "url": "https://towardsdatascience.com/model-drift-in-machine-learning-8023e3d08217/",
  "publish_date": "2021-08",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "e270e22521c0",
  "collected_at": "2026-10-02T00:32:03",
  "title": "Model Drift in Machine Learning | Towards Data Science",
  "body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](https://cdn-cookieyes.com/assets/images/close.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n  * Cookie\nBCTempID\n  * Duration\n10 minutes\n  * Description\nNo description available.\n\n\n  * Cookie\n__cf_bm\n  * Duration\n1 hour\n  * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n  * Cookie\nAWSALBCORS\n  * Duration\n7 days\n  * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n  * Cookie\n_cfuvid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n  * Cookie\nli_gc\n  * Duration\n6 months\n  * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n  * Cookie\n__hssrc\n  * Duration\nsession\n  * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n  * Cookie\n__hssc\n  * Duration\n1 hour\n  * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n  * Cookie\nwpEmojiSettingsSupports\n  * Duration\nsession\n  * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n  * Cookie\nBCSessionID\n  * Duration\n1 year 1 month 4 days\n  * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n  * Cookie\n_octo\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nlogged_in\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\n__Secure-YEC\n  * Duration\npast\n  * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n  * Cookie\n__eoi\n  * Duration\n6 months\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTGCORS\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\nlogin-status-p\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTG\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\ncsrf_token\n  * Duration\nsession\n  * Description\nNo description available.\n\n\n  * Cookie\ntoken_v2\n  * Duration\n1 day\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nD\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nPHPSESSID\n  * Duration\nsession\n  * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n  * Cookie\nVISITOR_PRIVACY_METADATA\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n  * Cookie\ncookietest\n  * Duration\nsession\n  * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n  * Cookie\n__Host-airtable-session\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\n__Host-airtable-session.sig\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\nm\n  * Duration\n1 year 1 month 4 days\n  * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n  * Cookie\nBIGipServer*\n  * Duration\nsession\n  * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n  * Cookie\n__cfruid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n  * Cookie\n_GRECAPTCHA\n  * Duration\n6 months\n  * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n  * Cookie\n__Secure-YNID\n  * Duration\n6 months\n  * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n  * Cookie\ncookieyes-consent\n  * Duration\n1 year\n  * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n  * Cookie\nlidc\n  * Duration\n1 day\n  * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n  * Cookie\nbrw\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nbrwConsent\n  * Duration\n5 minutes\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nWMF-Uniq\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nloom_anon_comment\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nloom_referral_video\n  * Duration\nsession\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nVISITOR_INFO1_LIVE\n  * Duration\n6 months\n  * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n  * Cookie\nyt-remote-connected-devices\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n  * Duration\nNever Expires\n  * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n  * Cookie\nyt-remote-device-id\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-name\n  * Duration\nsession\n  * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\nyt-remote-fast-check-period\n  * Duration\nsession\n  * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-app\n  * Duration\nsession\n  * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-available\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-installed\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\ncp_session\n  * Duration\n3 months\n  * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n  * Cookie\nloid\n  * Duration\n1 year 1 month 4 days\n  * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n  * Cookie\n__hstc\n  * Duration\n6 months\n  * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n  * Cookie\nhubspotutk\n  * Duration\n6 months\n  * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n  * Cookie\n_ga\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n  * Cookie\n_ga_*\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n  * Cookie\n__Host-psifi.analyticsTrace\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n__Host-psifi.analyticsTraceV2\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n_gh_sess\n  * Duration\nsession\n  * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n  * Cookie\nYSC\n  * Duration\nsession\n  * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n  * Cookie\najs_anonymous_id\n  * Duration\n1 year\n  * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n  * Cookie\nvuid\n  * Duration\n1 year 1 month 4 days\n  * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n  * Cookie\nAWSALB\n  * Duration\n7 days\n  * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n  * Cookie\nacq\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nacq.sig\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nptc\n  * Duration\n2 years\n  * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n  * Cookie\nmuc_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n  * Cookie\nguest_id_marketing\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\nguest_id_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\npersonalization_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n  * Cookie\nguest_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n  * Cookie\nbcookie\n  * Duration\n1 year\n  * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n  * Cookie\n__Secure-ROLLOUT_TOKEN\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n  * Cookie\nyt.innertube::nextId\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nyt.innertube::requests\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nsession_tracker\n  * Duration\nsession\n  * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# Model Drift in Machine Learning\nUnderstanding and Dealing with Model Drift\n[Kurtis Pykes](https://towardsdatascience.com/author/kurtispykes/)\nAugust 3, 20215 min read\n![Photo by Aral Tasher on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/08/0BqSqKe28QMQbdm7G-scaled.jpg)\nPhoto by [Aral Tasher](https://unsplash.com/@araltasher?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nAll things tend towards disorder. The second law of thermodynamics states \"as one goes forward in time, the net entropy (degree of disorder) of any isolated or closed system will always increase (or at least stay the same)\". Thus, nothing lasts forever. Our youth is not forever, the best becomes the worst, and our machine learning models degrades as time does its thing.\nThe world is not static, it's dynamic and continually changing. A spam email from the 2000s isn't the same as a spam email in 2021. The features used to detect fraudulent emails in 2021 would differ significantly from those of the 2000s - people got smarter, including scammers. If we attempted to use a model developed in the year 2000 to classify whether emails from the year 2021 are fraudulent or not, we could expect to see the predictive power of the model worsen in comparison to a fraudulent email from the year 2000. This paradigm describes a concept known as _model drift_.\nModel Drift is the decay of a model's predictive power as a result of alterations in the environment. If thingsstayed the same, i.e. the environment and the data, we should expect our machine learning models predictive power to remain constant. However, we all know the real world is ever changing. The changes in a real world environment affect the relationships between variables thus making predictions made by the model worse off. For example, the changes in the way spam emails are presented will affect our machine learning model's ability to detect spam emails.\nEssentially, we should expect a machine learning model to lose predictive power because over time, things change. If we do not detect a drifting model on time, the effects can be severely detrimental to our pipeline, services, and/or business as a whole. But how do we know when a model is drifting? It starts by understanding the ways a model may drift.\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n### The Different Types of Model Drift\nTo know how to deal with model drift, we must first understand the type of model drift we are dealing with and what is causing it.\n**Concept Drift**\nWhen we train a machine learning model on data, the model learns a function that maps the features to the target variable. As previously stated, if all things were static and nothing evolved over time, then we'd expect the relationship from the features to the target to hold true, thus the model should perform as it always has.\nHowever, the reality is that things do change. The statistical properties of the target variables change, and so does the meaning. Therefore, the mapping that was learned by the machine learning model is no longer suitable for the new environment - i.e. the idea of what a spammer was has evolved over time.\n**Data Drift**\nWith concept drift, the definition of a spammer changes - the statistical properties of the target variable change in unforeseen ways. Contrastingly, data drift refers to the change in the properties of the features. The underlying changes in the features cause the model to fail.\nData may drift because of:\n  * **Seasonality** - regular and predictable changes in data that recur every calendar year\n  * **Consumer Preferences -** what we prefer changes over time. What we like today may not be what we like tomorrow.\n  * Etc.\n\n\n### Detecting Model Drift\nBoth concept and data drift are a response to statistical changes in the data. Thus, using techniques to monitor the statistical properties, the model's predictions, and their correlation with other factors, does not seem like a bad idea.\nFor example, we can monitor the F1 score - depending on the problem - to determine whether our model is still performing as it should. Once the model drops below a specified F1 threshold, we can infer that our model is drifting. Or we can monitor the output of a prediction along with other features. For instance, if we see an increase or decrease in the number of fraudulent transactions at a rate that is severely different from that of active users, then we can infer that some drift may be occurring.\n### Overcoming Model Drift\nDetecting model drift is equivalent to admitting you have a problem. Once you've realized there's a problem, the next step is figuring out how to overcome the problem, and creating a plan. In the case of model drift, there are 2 popular plans for tackling the model drift problem.\nThe first method involves periodically retraining your machine learning model. This method is quite effective. If we are aware that a model degrades every 3 months, then a good idea would be to retrain the model every 2 months to ensure the performance of the model never falls below a predetermined threshold.\nAnother solution to address model drift is _online learning_. Online learning is a method of machine learning in which a model learns in real-time. The way it works is that data becomes available in a sequential manner and is used to update the best model which is being used for future data at each step.\n> **Disclaimer** : Executing online learning is extremely tricky but when it's done correctly, the results that can be achieved are phenomenal.\n### Final Thoughts\nAs time goes on, most things deteriorate; our bones get weaker, bananas go bad, and machine learning models lose their predictive power. This is a concept known as _model drift_. Both types of model drift involved statistical changes within the data but detecting model drift is still a difficult task. Many startups have popped up over the years to tackle this problem, and teams over the world have come up with a number of ways to detect and overcome the issue.\nThanks for Reading!\nIf you enjoyed this article, connect with me by subscribing **** to my** FRE**E [weekly newsletter](https://mailchi.mp/ef1f7700a873/sign-up). Never miss a post I make about Artificial Intelligence, Data Science, and Freelancing.\n### Related Articles\n> [**How to Frame a Product Goal as a Machine Learning Problem**](https://towardsdatascience.com/how-to-frame-a-product-goal-as-a-machine-learning-problem-a3c55aeb75b2)\n> [**3 Mistakes That Transformed My Machine Learning Career**](https://towardsdatascience.com/3-mistakes-that-transformed-my-machine-learning-career-de76679e0084)\n> [**5 Beginner-Friendly Machine Learning Projects**](https://towardsdatascience.com/5-beginner-friendly-machine-learning-projects-3475ad85167a)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657084566_vncpf1.jpeg)\nWritten By\n## [Kurtis Pykes](https://towardsdatascience.com/author/kurtispykes/)\n[See all from Kurtis Pykes](https://towardsdatascience.com/author/kurtispykes/)\n[Artificial Intelligence](https://towardsdatascience.com/tag/artificial-intelligence/)[Education](https://towardsdatascience.com/tag/education/)[Ideas](https://towardsdatascience.com/tag/ideas/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Technology](https://towardsdatascience.com/tag/technology/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fmodel-drift-in-machine-learning-8023e3d08217%2F&title=Model%20Drift%20in%20Machine%20Learning)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fmodel-drift-in-machine-learning-8023e3d08217%2F&title=Model%20Drift%20in%20Machine%20Learning)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fmodel-drift-in-machine-learning-8023e3d08217%2F&text=Model%20Drift%20in%20Machine%20Learning)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/05/1KKYZK_GTHja7MIKqVO26Sw-scaled.jpeg)](https://towardsdatascience.com/efficient-generalized-spherical-cnns-1493426362ca/)\n## [Efficient Generalized Spherical CNNs](https://towardsdatascience.com/efficient-generalized-spherical-cnns-1493426362ca/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nHybrid rotationally equivariant spherical CNNs\n[Jason McEwen](https://towardsdatascience.com/author/jasonmcewen/)\nMay 3, 202210 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/02/0C783ZfFIphu8eFdm-scaled.jpg)](https://towardsdatascience.com/qlora-how-to-fine-tune-an-llm-on-a-single-gpu-4e44d6b5be32/)\n## [QLoRA - How to Fine-Tune an LLM on a Single GPU](https://towardsdatascience.com/qlora-how-to-fine-tune-an-llm-on-a-single-gpu-4e44d6b5be32/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nAn introduction with Python example code (ft. Mistral-7b)\n[Shaw Talebi](https://towardsdatascience.com/author/shawhin/)\nFebruary 22, 202416 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/01/1_7Zn6x8hw3pYPca3aCt7AQ.png)](https://towardsdatascience.com/basic-statistics-you-need-to-know-for-data-science-1fdd290f59b5/)\n## [Basic Statistics You NEED to Know for Data Science](https://towardsdatascience.com/basic-statistics-you-need-to-know-for-data-science-1fdd290f59b5/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nFundamental statistic concepts to get you started on your Data Science journey\n[Terence Shin, MSc, MBA](https://towardsdatascience.com/author/terenceshin/)\nJanuary 13, 20205 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/09/1LU8hyWCshPEFMQcxaj21lw-scaled.jpeg)](https://towardsdatascience.com/how-to-effectively-define-and-validate-use-cases-932c284837fe/)\n## [How to effectively define and validate AI/ML Use Cases](https://towardsdatascience.com/how-to-effectively-define-and-validate-use-cases-932c284837fe/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nMost data professionals struggle to define Use Cases that transform into AI/ ML solutions that bring real value.\n[Przemek Pospieszny](https://towardsdatascience.com/author/przemek_pospieszny/)\nSeptember 9, 20209 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/09/129Hja14Ep12c5-XAlwcrrg.jpeg)](https://towardsdatascience.com/how-tiny-neural-networks-represent-basic-functions-8a24fce0e2d5/)\n## [How Tiny Neural Networks Represent Basic Functions](https://towardsdatascience.com/how-tiny-neural-networks-represent-basic-functions-8a24fce0e2d5/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nA gentle introduction to mechanistic interpretability through simple algorithmic examples\n[Amir Taubenfeld](https://towardsdatascience.com/author/taubenfeld9/)\nSeptember 10, 20249 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/0T75nt3vNPYCedINJ.png)](https://towardsdatascience.com/temporal-graph-learning-in-2024-feaa9371b8e2/)\n## [Temporal Graph Learning in 2024](https://towardsdatascience.com/temporal-graph-learning-in-2024-feaa9371b8e2/)\n[Deep Learning](https://towardsdatascience.com/category/artificial-intelligence/deep-learning/)\nContinue the journey for evolving networks\n[Shenyang(Andy) Huang](https://towardsdatascience.com/author/shenyanghuang1996/)\nJanuary 18, 202421 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/08/02QwB4nIJeP5KnoHR-scaled.jpg)](https://towardsdatascience.com/lets-build-deploy-automl-solution-in-5-minutes-4e5683635caf/)\n## [Let's Build & Deploy AutoML Solution in 5 Minutes](https://towardsdatascience.com/lets-build-deploy-automl-solution-in-5-minutes-4e5683635caf/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nFully automated machine learning API for classification tasks\n[Dario Radečić](https://towardsdatascience.com/author/radecicdario/)\nAugust 7, 20206 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/01/1VoGh6YjqDe5F9zN_kKC09A.jpeg)](https://towardsdatascience.com/interpretable-and-explainable-ner-with-lime-d643512c524/)\n## [Interpretable and Explainable NER with LIME](https://towardsdatascience.com/interpretable-and-explainable-ner-with-lime-d643512c524/)\n[Deep Learning](https://towardsdatascience.com/category/artificial-intelligence/deep-learning/)\nA step-by-step tutorial to learn how your NER model works\n[Walid Amamou](https://towardsdatascience.com/author/walidamamou/)\nJanuary 14, 20227 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2019/08/1GjXWopoR9zMlGZAg2Wz2bw.png)](https://towardsdatascience.com/ai-safety-how-do-you-prevent-adversarial-attacks-ede17480a24d/)\n## [AI Safety - How Do you Prevent Adversarial Attacks?](https://towardsdatascience.com/ai-safety-how-do-you-prevent-adversarial-attacks-ede17480a24d/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nA quick chat with IBM researchers Pin-Yu and Sijia about their recent papers with the two concepts of 'Pruning' and 'Block Changing'\n[Alex Moltzau](https://towardsdatascience.com/author/alexmoltzau/)\nAugust 15, 20195 min read\n\n\n  * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n  * [X](https://x.com/TDataScience)\n  * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n  * [Threads](https://www.threads.net/@towardsdatascience)\n  * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n####  Recommended Articles \nClose\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607631/image?etag=1781543525510&width=500&height=500) Critical Mistakes Companies Make When Integrating AI/ML into Their Processes ](https://towardsdatascience.com/critical-mistakes-companies-make-when-integrating-ai-ml-into-their-processes/ \"Critical Mistakes Companies Make When Integrating AI/ML into Their Processes\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500) How Relevance Models Foreshadowed Transformers for NLP ](https://towardsdatascience.com/how-relevance-models-foreshadowed-transformers-for-nlp/ \"How Relevance Models Foreshadowed Transformers for NLP\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n\n\n",
  "metadata": {
    "title": "Model Drift in Machine Learning | Towards Data Science",
    "description": "Understanding and Dealing with Model Drift",
    "keywords": null,
    "author": "Kurtis Pykes",
    "og:locale": "en_US",
    "og:type": "article",
    "og:title": "Model Drift in Machine Learning",
    "og:description": "Understanding and Dealing with Model Drift",
    "og:url": "https://towardsdatascience.com/model-drift-in-machine-learning-8023e3d08217/",
    "og:site_name": "Towards Data Science",
    "og:image": "https://assets.insightmediagroup.io/media/wp-content/uploads/2021/08/0BqSqKe28QMQbdm7G-scaled.jpg",
    "og:image:width": "2560",
    "og:image:height": "1707",
    "og:image:type": "image/jpeg",
    "twitter:card": "summary_large_image",
    "twitter:site": "@TDataScience",
    "twitter:creator": "@TDataScience",
    "twitter:label1": "Written by",
    "twitter:data1": "Kurtis Pykes",
    "twitter:label2": "Est. reading time",
    "twitter:data2": "5 min read",
    "article:published_time": "2021-08-03T13:53:43.000Z",
    "article:modified_time": "2021-08-03T13:53:43.000Z",
    "article:author": "https://towardsdatascience.com/author/kurtispykes/"
  },
  "paywall": {
    "listed_paid": true,
    "detected": false,
    "marker": "",
    "public_only": true,
    "policy": "付费/会员内容只保留页面公开可见部分，不绕过付费墙"
  },
  "crawl": {
    "success": true,
    "status_code": 200,
    "error": ""
  },
  "code_blocks": [],
  "images": [
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2021/08/0BqSqKe28QMQbdm7G-scaled.jpg",
      "alt": "Photo by Aral Tasher on Unsplash",
      "local_path": "attachments/e270e22521c0/images/000.jpg",
      "bytes": 529446,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2022/05/1KKYZK_GTHja7MIKqVO26Sw-scaled.jpeg",
      "alt": "",
      "local_path": "attachments/e270e22521c0/images/001.jpeg",
      "bytes": 753081,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2024/02/0C783ZfFIphu8eFdm-scaled.jpg",
      "alt": "",
      "local_path": "attachments/e270e22521c0/images/002.jpg",
      "bytes": 414304,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2020/01/1_7Zn6x8hw3pYPca3aCt7AQ.png",
      "alt": "",
      "local_path": "attachments/e270e22521c0/images/003.png",
      "bytes": 434388,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2020/09/1LU8hyWCshPEFMQcxaj21lw-scaled.jpeg",
      "alt": "",
      "local_path": "attachments/e270e22521c0/images/004.jpeg",
      "bytes": 494963,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2024/09/129Hja14Ep12c5-XAlwcrrg.jpeg",
      "alt": "",
      "local_path": "attachments/e270e22521c0/images/005.jpeg",
      "bytes": 140208,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/0T75nt3vNPYCedINJ.png",
      "alt": "",
      "local_path": "attachments/e270e22521c0/images/006.png",
      "bytes": 1927965,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2020/08/02QwB4nIJeP5KnoHR-scaled.jpg",
      "alt": "",
      "local_path": "attachments/e270e22521c0/images/007.jpg",
      "bytes": 184509,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2022/01/1VoGh6YjqDe5F9zN_kKC09A.jpeg",
      "alt": "",
      "local_path": "attachments/e270e22521c0/images/008.jpeg",
      "bytes": 567902,
      "downloaded": true
    },
    {
      "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2019/08/1GjXWopoR9zMlGZAg2Wz2bw.png",
      "alt": "",
      "local_path": "attachments/e270e22521c0/images/009.png",
      "bytes": 353086,
      "downloaded": true
    }
  ],
  "audio": [],
  "videos": [
    {
      "src": "https://www.youtube.com/c/TowardsDataScience",
      "quality": "unavailable",
      "local_path": "",
      "note": "无1080p且无720p，未交付更低清晰度"
    }
  ],
  "links": [
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/latest/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/editors-pick/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/the-variable/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/shipai/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/submissions/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/artificial-intelligence/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/kurtispykes/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/how-to-frame-a-product-goal-as-a-machine-learning-problem-a3c55aeb75b2"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/3-mistakes-that-transformed-my-machine-learning-career-de76679e0084"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/5-beginner-friendly-machine-learning-projects-3475ad85167a"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/artificial-intelligence/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/education/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/ideas/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/machine-learning/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/technology/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/efficient-generalized-spherical-cnns-1493426362ca/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/artificial-intelligence/machine-learning/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/jasonmcewen/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/qlora-how-to-fine-tune-an-llm-on-a-single-gpu-4e44d6b5be32/"
    },
    {
      "type": "external",
      "href": "https://write.insightmediagroup.io"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/company/towards-data-science/"
    },
    {
      "type": "external",
      "href": "https://x.com/TDataScience"
    },
    {
      "type": "external",
      "href": "https://unsplash.com/@araltasher"
    },
    {
      "type": "external",
      "href": "https://unsplash.com"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh/ai-engineer"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh/computer-science"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh/machine-learning"
    },
    {
      "type": "external",
      "href": "https://mailchi.mp/ef1f7700a873/sign-up"
    },
    {
      "type": "external",
      "href": "https://www.facebook.com/sharer/sharer.php?title=Model+Drift+in+Machine+Learning&u=https%3A%2F%2Ftowardsdatascience.com%2Fmodel-drift-in-machine-learning-8023e3d08217%2F"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/shareArticle?mini=true&title=Model+Drift+in+Machine+Learning&url=https%3A%2F%2Ftowardsdatascience.com%2Fmodel-drift-in-machine-learning-8023e3d08217%2F"
    },
    {
      "type": "external",
      "href": "https://x.com/share?text=Model+Drift+in+Machine+Learning&url=https%3A%2F%2Ftowardsdatascience.com%2Fmodel-drift-in-machine-learning-8023e3d08217%2F"
    },
    {
      "type": "external",
      "href": "https://www.youtube.com/c/TowardsDataScience"
    },
    {
      "type": "external",
      "href": "https://www.threads.net/@towardsdatascience"
    },
    {
      "type": "external",
      "href": "https://bsky.app/profile/towardsdatascience.com"
    },
    {
      "type": "external",
      "href": "https://thenewstack.io"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh"
    },
    {
      "type": "external",
      "href": "https://insightmediagroup.io/creator-network"
    }
  ],
  "repair_version": 2,
  "repaired_at": "2026-10-14T11:11:49Z",
  "source_url": "https://towardsdatascience.com/model-drift-in-machine-learning-8023e3d08217/",
  "delivery_status": "excluded_paid",
  "discovery_status": "excluded_paid",
  "missing_reason": "按原约定排除付费/会员全文"
}