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Their lofty goal is to go from a small example set to a broader inductive generalization.\nNaturally, for successful (and dependable) functioning, they need to depend on the critical assumption - _the input data distribution does not drift away too much over time_.\nBecause, if that happens, then **a successful model is only successful at a single point in time**. Over time, the model will make inductive generalizations based on a distribution that is not exactly what it was trained on, and performance will degrade.\nSo, we need monitoring and (timely) detection of any data-drift in the **MLOps or ModelOps pipeline** for any (continually) successful ML model deployment. Apart from regular MLops, the concept of data drift is important for ML explainability or [**Explainable AI (xAI)**](https://www.ibm.com/watson/explainable-ai) field too.\n_Where exactly a data drift monitoring block should sit_ in an end-to-end MLops cycle is nicely visualized in the following article.\n> [**Essential guide to Machine Learning Model Monitoring in Production**](https://towardsdatascience.com/essential-guide-to-machine-learning-model-monitoring-in-production-2fbb36985108)\nA lot of articles on this topic discuss the shape and statistical properties of the input distributions. In this article, we will take the example of (one or more) data streams feeding into ML models in the form of time series and visualize those variations to understand the concept clearly. Additionally, we will also talk about a few not-so-obvious sources of data drifts in an industrial context.\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## Data drift is like a box of chocolates...\nYou never know what you're gonna get (next)! Let's see some obvious and not-so-obvious examples...\n### Level shift? Obviously\nThis is an obvious data drift. In many unsupervised model settings, this will also be called an '_**outlier**_ ' or '_**anomaly**_ '. Also note, drift may not be permanent and can be short-lived. Both the start and end of the drift should be identified quickly.\n### Variance shift? Subtler\nThis is slightly harder to detect than level shift. See the illustration below.\n### Variance decrease? That's a drift too\nAt a high level, business/ process people (or your customer) will tell you that high variance is bad. Yes, they are almost always right. But from a data drift point of view, you have to watch for any chance - even a sudden decrease in the variance. Statistically, that is a change in the distribution properties too.\n### Peak shifts inside a period\nThis one is more subtle and hard to detect. I have tried to illustrate the drift with additional annotations like _peak_ and _period_. Did you see what really happened here? The data comes in a periodic fashion and there is a peak within each period. **The relative position of that peak (within a period) shifted** for some time before it came back to normal.\nDo you see the problem? Simple statistical properties may not catch this drift. The mean and variance (other moments too) may look similar. How do you catch it then? Interestingly, some simple **spectral analysis** will indicate a shift. So, you have to bring in some **knowledge of signal processing** to catch this data drift.\n### Contextual data - another way to catch the drift\nIf you are also monitoring a logging stream that has fixed-time indicators, like a shift start, or a machine reset, that can sometimes help catch these temporal shifts in an intuitive manner. The data monitoring has to ensure excellent time-synchronization of course. In the following illustration, the anchor events and the relative distance of the peaks from those anchors will indicate a drift.\n### Phase-shift/delay\nThis is a killer, in a true sense. For ML models training and inferencing on time-series data, a slight phase delay can generate totally wrong predictions. Basically, the model was trained like \"If _X_1 and _X_2 were similar then predict _Y_0, otherwise predict _Y_1\".\nNow, look at the situation below and realize what would happen with out-of-phase data streams. The top data stream is fine, the bottom one drifted slightly. It is possible that the data stream is fine, only the acquisition was delayed and fell out of sync.\n### Some are drift, some not\nIn industrial or manufacturing scenarios, process recipes and settings change all the time. There may also be a scheduled changelog. This kind of contextual data needs to be monitored for identifying correct data drift. Here is the visual illustration,\n### Measurement/Sensor drift\nThis is also one of the hardest to detect and manage. The input data streams and the generative processes may be fine but there may be drift on the sensor (that is measuring the incoming data and feeding to the ML model or monitoring system).\nA monitoring system (consisting of an ML model and an alert generating module) may detect a drift and send a wrong recommendation signal to the core process layer thinking that the fundamental process has shifted. That would be a misjudgment and bad recommendation.\nThis is also an example where a **concept drift (the model or the physical dynamics of the sensor itself) can be mistaken for a data drift**. That's an error to be avoided at all costs.\n## Summary\nWe discussed the concept of data drift in a time-series sense by showing some visual illustrations. It is obvious that catching and analyzing data drift is not a trivial problem, even in the simplest one-dimensional setting. Additional techniques like signal processing or merging with contextual data may be needed to correctly identify the onset and nature of data drift.\nWhat are the mitigations? There is no one answer and it totally depends on the industry and the specific application area. This article goes through some of them systematically. Give it a read.\n> [**\"My data drifted. What's next?\" How to handle ML model drift in production.**](https://towardsdatascience.com/my-data-drifted-whats-next-how-to-handle-ml-model-drift-in-production-78719ef007b1)\n* * *\n_Loved the article? Become a_[ _**Medium member**_](https://medium.com/@tirthajyoti/membership) _to continue_ _**learning without limits**_ _. I'll receive a portion of your membership fee if you use the following link,__**with no extra cost to you**_ _._\n> [**Join Medium with my referral link - Tirthajyoti Sarkar**](https://medium.com/@tirthajyoti/membership)\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. 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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# Data drift: It can come at you from anywhere\nThe concept of data drift is illustrated visually in various shapes and forms.\n[Tirthajyoti Sarkar](https://towardsdatascience.com/author/tirthajyoti/)\nJanuary 19, 20225 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/01/1TnU7qZ0SUvvjCMM7E6e91g.png)\n## A short introduction to data drift\nMachine learning (ML) models, at least in their present incarnations, are little more than [**powerful inductive machines**](https://medium.com/tech-cult-heartbeat/introduction-to-inductive-learning-in-artificial-intelligence-dafc2796405b). Their lofty goal is to go from a small example set to a broader inductive generalization.\nNaturally, for successful (and dependable) functioning, they need to depend on the critical assumption - _the input data distribution does not drift away too much over time_.\nBecause, if that happens, then **a successful model is only successful at a single point in time**. Over time, the model will make inductive generalizations based on a distribution that is not exactly what it was trained on, and performance will degrade.\nSo, we need monitoring and (timely) detection of any data-drift in the **MLOps or ModelOps pipeline** for any (continually) successful ML model deployment. Apart from regular MLops, the concept of data drift is important for ML explainability or [**Explainable AI (xAI)**](https://www.ibm.com/watson/explainable-ai) field too.\n_Where exactly a data drift monitoring block should sit_ in an end-to-end MLops cycle is nicely visualized in the following article.\n> [**Essential guide to Machine Learning Model Monitoring in Production**](https://towardsdatascience.com/essential-guide-to-machine-learning-model-monitoring-in-production-2fbb36985108)\nA lot of articles on this topic discuss the shape and statistical properties of the input distributions. In this article, we will take the example of (one or more) data streams feeding into ML models in the form of time series and visualize those variations to understand the concept clearly. Additionally, we will also talk about a few not-so-obvious sources of data drifts in an industrial context.\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## Data drift is like a box of chocolates...\nYou never know what you're gonna get (next)! Let's see some obvious and not-so-obvious examples...\n### Level shift? Obviously\nThis is an obvious data drift. In many unsupervised model settings, this will also be called an '_**outlier**_ ' or '_**anomaly**_ '. Also note, drift may not be permanent and can be short-lived. Both the start and end of the drift should be identified quickly.\n### Variance shift? Subtler\nThis is slightly harder to detect than level shift. See the illustration below.\n### Variance decrease? That's a drift too\nAt a high level, business/ process people (or your customer) will tell you that high variance is bad. Yes, they are almost always right. But from a data drift point of view, you have to watch for any chance - even a sudden decrease in the variance. Statistically, that is a change in the distribution properties too.\n### Peak shifts inside a period\nThis one is more subtle and hard to detect. I have tried to illustrate the drift with additional annotations like _peak_ and _period_. Did you see what really happened here? The data comes in a periodic fashion and there is a peak within each period. **The relative position of that peak (within a period) shifted** for some time before it came back to normal.\nDo you see the problem? Simple statistical properties may not catch this drift. The mean and variance (other moments too) may look similar. How do you catch it then? Interestingly, some simple **spectral analysis** will indicate a shift. So, you have to bring in some **knowledge of signal processing** to catch this data drift.\n### Contextual data - another way to catch the drift\nIf you are also monitoring a logging stream that has fixed-time indicators, like a shift start, or a machine reset, that can sometimes help catch these temporal shifts in an intuitive manner. The data monitoring has to ensure excellent time-synchronization of course. In the following illustration, the anchor events and the relative distance of the peaks from those anchors will indicate a drift.\n### Phase-shift/delay\nThis is a killer, in a true sense. For ML models training and inferencing on time-series data, a slight phase delay can generate totally wrong predictions. Basically, the model was trained like \"If _X_1 and _X_2 were similar then predict _Y_0, otherwise predict _Y_1\".\nNow, look at the situation below and realize what would happen with out-of-phase data streams. The top data stream is fine, the bottom one drifted slightly. It is possible that the data stream is fine, only the acquisition was delayed and fell out of sync.\n### Some are drift, some not\nIn industrial or manufacturing scenarios, process recipes and settings change all the time. There may also be a scheduled changelog. This kind of contextual data needs to be monitored for identifying correct data drift. Here is the visual illustration,\n### Measurement/Sensor drift\nThis is also one of the hardest to detect and manage. The input data streams and the generative processes may be fine but there may be drift on the sensor (that is measuring the incoming data and feeding to the ML model or monitoring system).\nA monitoring system (consisting of an ML model and an alert generating module) may detect a drift and send a wrong recommendation signal to the core process layer thinking that the fundamental process has shifted. That would be a misjudgment and bad recommendation.\nThis is also an example where a **concept drift (the model or the physical dynamics of the sensor itself) can be mistaken for a data drift**. That's an error to be avoided at all costs.\n## Summary\nWe discussed the concept of data drift in a time-series sense by showing some visual illustrations. It is obvious that catching and analyzing data drift is not a trivial problem, even in the simplest one-dimensional setting. Additional techniques like signal processing or merging with contextual data may be needed to correctly identify the onset and nature of data drift.\nWhat are the mitigations? There is no one answer and it totally depends on the industry and the specific application area. This article goes through some of them systematically. Give it a read.\n> [**\"My data drifted. What's next?\" How to handle ML model drift in production.**](https://towardsdatascience.com/my-data-drifted-whats-next-how-to-handle-ml-model-drift-in-production-78719ef007b1)\n* * *\n_Loved the article? Become a_[ _**Medium member**_](https://medium.com/@tirthajyoti/membership) _to continue_ _**learning without limits**_ _. I'll receive a portion of your membership fee if you use the following link,__**with no extra cost to you**_ _._\n> [**Join Medium with my referral link - Tirthajyoti Sarkar**](https://medium.com/@tirthajyoti/membership)\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/1787657105381_zw8h6h.jpeg)\nWritten By\n## [Tirthajyoti Sarkar](https://towardsdatascience.com/author/tirthajyoti/)\n[See all from Tirthajyoti Sarkar](https://towardsdatascience.com/author/tirthajyoti/)\n[Artificial Intelligence](https://towardsdatascience.com/tag/artificial-intelligence/)[Data Science](https://towardsdatascience.com/tag/data-science/)[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%2Fdata-drift-it-can-come-at-you-from-anywhere-b78eb186855%2F&title=Data%20drift%3A%20It%20can%20come%20at%20you%20from%20anywhere)\n * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fdata-drift-it-can-come-at-you-from-anywhere-b78eb186855%2F&title=Data%20drift%3A%20It%20can%20come%20at%20you%20from%20anywhere)\n * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fdata-drift-it-can-come-at-you-from-anywhere-b78eb186855%2F&text=Data%20drift%3A%20It%20can%20come%20at%20you%20from%20anywhere)\n\n\nTowards Data Science is a community publication. 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