Mitigating model drift in machine learning

A machine learning model does not stay accurate forever. After an ML model is deployed, the data it sees in the real world often shifts away from the data it was trained on. Over time, the model’s predictions start to degrade in quality, which is known as model drift. In other words, the model “drifts” from its original accuracy and purpose as the world changes around it. 

Data quality and schema changes are separate integrity issues that mimic drift and must be monitored independently.

In this article, we’ll break down what model drift is and why it happens; the different forms it takes, such as concept drift and data drift; and how organizations detect and combat model drift. We’ll also look at examples in real-time analytics, fraud detection, and advertising technology (AdTech), which are industries where staying ahead of model drift is especially important. You’ll understand how to keep your models performing well over time, and we’ll discuss how today’s data platforms, such as Aerospike’s real-time database, help with managing model drift.

What is model drift?

Model drift refers to a machine learning model’s tendency to lose predictive accuracy over time when it’s deployed in the real world. In essence, the model’s performance drifts downward compared to its initial baseline. This usually happens because the statistical properties of the data, or the relationship between inputs and outputs, change after the model has been trained. As new patterns emerge in production data that weren’t present in the training set, the model’s old learned rules no longer hold, resulting in more errors. Shifts degrade performance when they move data outside the model’s learned decision boundaries; the effect depends on model capacity, calibration, and training coverage.

Sometimes “model drift” is used as a broad term encompassing any performance decline, while more specific terms describe the cause. Two subtypes are data drift and concept drift:

• Data drift refers to changes in the input data distribution. The incoming features start to look different from the training data. For example, if a model was trained on customer behavior from last year, but this year the demographics of the customers using the product have shifted, feature statistics such as age or location may no longer match the training profile. The model is now seeing different ingredients from what it was originally tuned for.

• Concept drift refers to changes in the relationship between inputs and the target output. In other words, the underlying concept that the model is predicting has evolved. The model’s learned decision boundaries or patterns no longer apply because what we are trying to predict, or the way features map to the prediction, has changed. For example, if a spam detection model was trained a few years ago, the definition and characteristics of a spam email message have evolved. Spammers now use new language and tactics, so the old rules don’t catch them. The model needs to learn the updated concept of spam. Concept drift is often the culprit behind model drift, although model drift as a symptom just means the accuracy dropped without initially knowing why.

There are also related concepts, such as target drift or label drift, which is a change in the distribution of the outcome variable. In many cases, this overlaps with concept drift; if the frequencies of certain outcomes change, such as fraud cases becoming more or less common, it may indicate that the concept itself or its prevalence has shifted. 

In practice, multiple types of drift occur together, and practitioners sometimes use these terms interchangeably. The core idea is that the world generating your data has changed in some way, and the model needs attention.

Types and causes of model drift

Why do models drift in the first place? In general, it’s because the world is constantly changing. But those changes manifest in different ways. Here are the major types/causes of drift that data science teams need to address:

Concept drift and changing relationships

Concept drift occurs when the statistical relationship between features (input) and the target outcome changes over time. The model starts giving wrong answers because the patterns it learned are no longer valid. Concept drift can happen in a few patterns:

Seasonal drift

The concept shifts in a recurring, periodic way. For example, consumer behavior in retail often has seasonality. A model predicting demand might see its accuracy drop during the holiday season if it were trained on mostly spring/summer data. 

For example, take snow shovels. A model trained in summer would underpredict winter sales because every late autumn, the concept of “likely to buy a shovel” changes dramatically. The drift here isn’t random; it follows a seasonal cycle of weather or holiday-related patterns that repeat.

Sudden drift

An abrupt, unexpected change in the relationship occurs. An external event renders the old model logic obsolete almost overnight. A classic example was the COVID-19 pandemic. Consumer and business behaviors shifted abruptly in March 2020, so models trained on pre-2020 data started failing when faced with pandemic-era patterns. Another example: a viral trend or news event suddenly changes user behavior. If a model was predicting demand for travel tickets and a travel ban is imposed, the prior relationships no longer predict the outcome. The model’s world changed in a blink.

Gradual drift

The change happens slowly and incrementally over time. Many adversarial domains fall here. For instance, spam detection or fraud detection is a cat-and-mouse game. Spammers and fraudsters continually adapt and introduce new techniques in a slow evolution, while protective models try to catch up. A spam filter’s performance might erode month by month as scammers find new ways to mimic legitimate email messages. If you looked at it day to day, you might not notice, but over a year, the drift is clear. Any static model in a dynamic environment gradually becomes stale if not updated.

In all these cases of concept drift, the definition of what the model is predicting (or the way features map to that definition) has changed. Seasonal and sudden drift are often noticeable through obvious events or time windows, while gradual drift requires monitoring to spot the slow decline.

Data drift and distribution shifts

Data drift, also known as covariate shift or feature drift, occurs when the distribution of the input data changes over time, even if the fundamental task remains the same. Unlike concept drift, the target definition hasn’t necessarily changed; rather, the input profile feeding the model is now different, which still throws the model off.

For example, imagine an e-commerce recommendation model trained when the user base was mostly young adults. If the platform gains popularity among older users, the input distributions such as age, preferences, and buying patterns shift. The model might start to perform poorly because it was calibrated to a younger demographic’s data. 

Another example: a retail sales forecasting model could see data drift if a competitor or product enters the market, because the features related to competition or product mix now have values/frequencies outside the original model training range. 

Data drift is caused by external trends, seasonality in features, or changes in data collection. Data drift also leads to model drift because the model is operating on unfamiliar inputs. 

However, data drift might or might not imply concept drift. It’s possible the input distribution shifts while the concept remains stable, and the model could potentially adapt if it had seen such variations, such as through seasonal or environmental changes that affect the data but not the relationship between inputs and outputs. But often, data drift eventually causes the model logic to misalign, causing poorer performance. 

Upstream data changes or pipeline issues

Not all model performance issues stem from the external world’s changes; some come from changes in the data pipeline or system feeding the model. An upstream data change means some aspect of how data is collected, processed, or formatted has changed, and the model wasn’t built or updated to handle it.

For instance, an engineering team might switch the units of a field from metric to imperial, record temperatures in Fahrenheit instead of Celsius, or currency in euros instead of dollars, without modelers realizing it. The live model then gets inputs in unexpected units and starts making large errors. 

Similarly, if a data source stops collecting a particular feature and starts sending nulls or a new categorical value, a model drifts because it’s getting broken or differently scaled data. In essence, the model’s environment changed due to data handling, not an underlying concept shift, but the effect on performance is the same. Upstream changes are often an overlooked cause of model drift and highlight why monitoring data quality and schema is part of the issue.

Why model drift matters 

Model drift is not just a technical nuisance. It has business and operational consequences. When a model’s performance degrades, decisions or actions driven by that model degrade along with it. This can lead to:

Faulty business decisions

Organizations rely on artificial intelligence (AI) models for recommendations, forecasts, and risk assessments. If an AI model drifts and starts giving wrong predictions, it loses money and misses opportunities. For example, a pricing model suffering drift might consistently underprice products, or a credit risk model might approve bad loans. 

Lost customers or revenue

In customer-facing applications, model drift hurts the user experience. If an online recommendation engine that worked well last month starts suggesting irrelevant content as user preferences drift, users will leave. In AdTech, a model that no longer targets the right audience means ad budget is wasted, and sales drop. Allowing a model to decay costs money.

Operational issues

From a machine learning operations (MLOps) perspective, undetected model drift leads to a scramble when people finally notice it. If the business realizes the model isn’t performing, data science teams must rush to diagnose the cause (Is it data drift? Concept drift? A pipeline error?) and retrain or fix the model. This unplanned work disrupts normal operations. It’s more efficient to catch drift early or prevent it, rather than react after damage. Indeed, monitoring for drift is considered a core part of AI governance and ML observability best practices. It’s like an early warning system to avoid bigger problems.

Erosion of trust and AI value

If users or stakeholders notice that the AI’s predictions have become inconsistent or wrong, they may lose trust in the system. This is a more subtle consequence but important, because it sets back an organization’s AI adoption if the first deployments age badly. Consistently maintaining model performance through drift management helps sustain confidence in AI systems over the long term.

In short, model drift affects the bottom line and team efficiency. It’s why mature AI teams emphasize monitoring model performance in production and have processes to update models when they start to degrade

Detecting model drift

How do you know if a model is drifting? Catching model drift requires monitoring both the model’s outputs and the data it’s handling. There are a few approaches, often used together, to detect drift:

• Monitor model quality metrics with ground truth: The most direct way to detect drift is by looking at the model accuracy or error rate over time on new data. If you have a stream of ground truth labels coming in, such as, in a fraud model, eventually learning which transactions were fraudulent, you periodically compute the model’s performance on recent data. A drop in performance metrics such as accuracy, precision, or an increase in error rate compared with the original baseline is a red flag that the model is drifting. Many organizations set up continuous evaluation pipelines. For instance, every week, validate the model on the latest data where you have outcomes, and compare it with last week’s performance. If the degradation exceeds a threshold, that triggers an alert. This approach pinpoints model drift because the model quality has worsened.

• Monitor data and prediction drift: Often, in production, you might not get ground truth labels right away. Think of a model that predicts customer lifetime value; you’ll only know the true value much later. In such cases, rely on proxy metrics to sense drift. Two common proxies are input data distributions and model prediction distributions. Essentially, you ask:

  • Has the data feeding the model changed since training? 

  • Have the model’s outputs changed in distribution? 

If yes, it could indicate drift. Data scientists use statistical tests to measure differences between two datasets, typically comparing recent production data with the training data or a moving baseline. 

For example, the Kolmogorov–Smirnov (K-S) test determines whether two samples come from the same distribution, useful for numeric features. A significant K-S test result means the feature’s distribution has shifted, suggesting data drift. 

Another measure, the Wasserstein or Earth Mover’s Distance, computes how far the distribution has moved, capturing complex differences between feature distributions. 

For categorical features, a popular metric is the Population Stability Index (PSI), which compares category frequency buckets between two samples. A PSI above a certain threshold, often >0.2, indicates drift in that feature. 

These statistical detectors run continuously on streaming data. If multiple features show large drift, it’s a sign that the model’s input environment is changing. Likewise, if the pattern of model predictions, such as the distribution of predicted probabilities, shifts drastically from earlier behavior, it may mean the model is encountering a new type of data.

• Track correlation changes and other signals: More nuanced signals also help. For instance, if certain relationships between features start to change, such as feature A used to correlate strongly with feature B, but now it doesn’t, that hints at an underlying concept change. In domain-specific cases, there may be custom heuristics. For example, in an Internet of Things (IoT) anomaly detection system, an increase in the frequency of alerts could signal drift in what is considered normal. The key is setting up ML observability with dashboards or systems that continuously watch these indicators in real time.

In practice, teams combine these approaches. For example, monitor a live accuracy metric alongside multiple data drift metrics. Today’s ML monitoring tools often include a suite of drift detection methods. In fact, drift detection itself sometimes produces false alarms, because random fluctuation looks like drift, so human judgment is needed to confirm whether an action such as retraining is warranted. Nonetheless, it’s better to have early warnings because they help catch issues before they cause harm.

Mitigating and preventing model drift

Knowing that drift will happen eventually, how do we manage it and reduce its impact? Mitigating model drift involves both reactive and proactive strategies:

Retrain models regularly with fresh data

The most straightforward remedy for model drift is to update the model with newer data that reflects the latest patterns. Many companies establish a retraining schedule, such as retraining the model every month or whenever a certain amount of new data has accumulated. By feeding the model recent examples, you realign it with the current state of the world. This ranges from rebuilding the model from scratch with an updated dataset to implementing online learning that updates the model continuously on each new data point. The approach depends on the use case. 

For instance, a recommendation engine might be retrained nightly, while a fraud detection model for credit cards might be updated weekly to incorporate the latest fraud signatures. The downside of retraining is the cost and effort; it requires a pipeline to gather and label new data, retrain, and deploy. But it is often necessary. Periodically retraining and tuning the model as data evolves is considered a best practice to prevent excessive drift. Successful AI teams put processes in place to monitor performance and update models before they become stale.

Automate drift monitoring and triggers

As with detection, automation is key. Many organizations set up automated alerts when drift metrics exceed thresholds. Some advanced systems go further, such as rolling back to a previous model if a newly deployed model shows sudden drift, or incorporating new data and training a candidate model when drift is detected. AI monitoring tools flag when a model’s accuracy falls below a preset threshold and even identify which inputs caused the drift, so those can be used in retraining. Automation reduces the burden on data science teams and catches issues faster than human observation alone.

Use robust data pipelines and validation

To address the upstream data change drift, check your data pipeline. This means validating data schemas, ranges, and distributions before the data reaches the model. If a feed starts giving all zeros or a new category, your system should catch it and either handle it or alert engineers. Some teams implement constraints, such as “temperature cannot jump by more than X in one hour” or anomaly detectors on input data. 

Ensuring training data and serving data remain consistent with no training/serving skew also helps. This is why the rise of feature stores has been important. A feature store means the same code generates features for training and inference, preventing inadvertent drifts caused by mismatched calculations. In fact, storing and updating features in a central store makes it easier to refresh training datasets continuously to reflect recent data, which addresses data drift by keeping training data current.

Design for real-time updates where feasible

In fast-paced environments, waiting a week or a month to retrain might be too slow. Leading tech companies design systems for near-real-time model updating. One notable example is TikTok’s recommendation algorithm. It continuously updates its models with streaming user interaction data, so it’s always adapting to the latest trends and user behavior. With a streaming data pipeline using technologies such as Kafka and Flink, they perform online training so model drift is mitigated on the fly. 

Not every organization needs to be this real-time, but the principle is: if your data changes rapidly, consider an infrastructure that learns and deploys changes rapidly, too. Even if full online learning isn’t possible, more frequent mini-batch retraining or updating certain model components, such as recalibrating a threshold or refreshing a few tree leaves, helps keep the model fresher. The tradeoff is complexity and cost, but for systems such as fraud and ads, it pays off in accuracy.

Use fallback rules or human-in-the-loop interventions

When a model is suspected to have drifted, but a retrain will take time, organizations may implement stopgap measures. For example, if a fraud detection model’s performance is dropping, the team might tighten some manual rules in the interim, such as flagging certain transactions for review until the model is retrained on the new pattern. 

Similarly, interventions such as adjusting the decision threshold of a classifier help if you notice drift. Maybe the model is still useful, but the score cutoff for “accept/reject” needs tweaking based on new base rates. In some cases, switching to a simpler decision system temporarily is wise. 

For instance, an e-commerce site might revert to a basic bestsellers recommendation if its more sophisticated model drifts, until it’s fixed. These measures mean the business keeps running safely. Of course, they are not long-term substitutes for fixing the model, but they reduce damage during the drift.

Improve the model with drift-robust techniques

Some modeling approaches help handle drift. Ensembles of models are sometimes better, especially if they are trained on different time periods; the ensemble is more resilient if one component drifts more slowly. Some models are designed to give more weight to recent data. Online gradient boosting, for example, reduces older observations’ influence. 

Additionally, including time as a feature or context helps a model discern seasonal drifts. In recurring drift situations, you might retrain models just before expected seasonal shifts, such as retraining a retail model every November before the holiday season. Some research algorithms are specifically for drift adaptation, such as using adaptive sliding window techniques and integrating drift detectors like ADWIN to trigger model updates. 

Finally, build a unified MLOps environment where data, models, and monitoring are linked. If the data engineers, ML engineers, and business analysts all see model performance dashboards, it’s easier to coordinate a response to drift. One example is a champion/challenger setup, where a new model is tested against the old in shadow mode, making sure it undoes the drift before full deployment. 

The overarching best practice is continuous learning: Treat model training not as a one-and-done, but as an ongoing process where the model is a living object that needs care throughout its life. With a combination of monitoring, retraining, pipeline rigor, and quick reactions, organizations help keep model drift in check. 

Real-world examples of model drift

Next, let’s examine a few examples where model drift is a challenge and how it appears in each.

Model drift in real-time analytics

“Real-time analytics” refers to systems that process and analyze data continuously as events happen, providing insights or decisions with little delay. Examples include live dashboards, automated decision engines for stock trading or sensor monitoring, and personalized content feeds that update frequently. In these scenarios, model drift is especially important because the data is not just changing over months; it’s changing by the minute, and decisions are being made on the fly.

In real-time analytics, patterns emerge and fade quickly. A model that was accurate last week might start faltering this week if user behavior or external conditions changed. 

For instance, consider algorithmic trading in financial markets: a model may exploit a certain market pattern, but as traders react and regimes shift, that pattern disappears or inverts, causing the model to lose money. Without rapid adaptation, a drifted model in this context leads to immediate losses. Or think about monitoring network traffic for intrusions. Attackers might start using a new technique, and the real-time anomaly detection model might begin missing these events until it’s updated.

Because of these stakes, tracking model drift in real time becomes a necessary part of the analytics system. Organizations with autonomous or agent-driven systems measure how quickly AI models degrade as real-world conditions change as a key metric, alongside metrics such as data freshness and latency. In other words, the speed of drift is itself monitored in real-time environments. If a model’s performance decays faster than expected, it alerts engineers.

Real-time analytics often uses streaming data architectures such as Kafka or Flink for quicker model updates. Rather than using static models that retrain only nightly, today’s real-time systems continuously feed new data to models. This might involve updating the model parameters on the fly or periodically retraining on a rolling window of the latest data. This shortens the time between data drift and model update. Continuous online learning effectively counteracts drift. A case in point is TikTok’s recommendation engine, which continuously updates its models with fresh data, mitigating drift and keeping recommendations relevant for users hour by hour.

In practice, not every real-time analytics example uses full online learning, because it is complex and expensive. But at least they retrain more often than traditional batch systems. They also tend to have strong monitoring; it logs each model prediction in real time and computes aggregate metrics in sliding windows to catch issues. Real-time AI systems are like living organisms; they continuously learn and adapt, so if any part, such as a model, stops adapting or drifts, the whole organism suffers.

Model drift in fraud detection

Fraud detection is a classic example of a domain with ever-evolving patterns, which makes it susceptible to model drift. Whether it’s credit card fraud, online transaction fraud, or spam detection, the adversary on the other side constantly changes tactics to avoid detection. This means the definition of “fraudulent” behavior is continuously shifting, a clear case of concept drift.

Consider a credit card fraud detection model. It might be trained on historical fraudulent versus legitimate transactions. Fraudsters, however, observe what triggers alarms and modify their behavior. Over time, they start making purchases in ways that look more like a normal customer, such as smaller amounts or mimicking spending patterns. Suddenly, the old fraud model, which was designed to catch the previous patterns, starts missing these new fraudulent transactions. If retraining doesn’t keep up with the fraudsters, the model’s recall and precision drop, and more bad transactions slip through.

Gradual drift is common here: fraud techniques often change bit by bit. For example, email spam of a decade ago was full of obvious keywords such as “lottery” or weird fonts. A spam filter from that era is useless today because spam tactics have gradually become more sophisticated. The same goes for financial fraud; schemes come and go, and models must be updated to include new fraud patterns, or else they forget to guard against them.

The implications of drift in fraud detection are serious. If the model isn’t catching fraud, the company could lose money, or customers could be harmed. On the other hand, if the model drifts in a way that it starts flagging too many legitimate transactions as false positives, that hurts customer experience and trust. So both precision and recall are important to maintain, and drift degrades either or both.

How do organizations manage drift here? Continuous monitoring and frequent updates are key. Many banks and payment companies retrain fraud models regularly, using the latest confirmed fraud cases to update the model’s knowledge. They also often use a layered approach: automated models plus rule-based systems plus human analysts. If the model starts to drift, rules and analysts might catch what it misses in the interim. 

For example, if a new fraud pattern emerges, such as a coordinated fraud attack using a new method, the model might miss it at first. But analysts might notice and put a quick rule in place (“block transactions from XYZ merchant for now”) until the model is retrained to recognize the pattern.

Another technique is to use adaptive or online learning. Some fraud systems use online learning algorithms that update model weights with each new confirmed fraud instance, almost in real time, so the model is always learning the latest behavior. However, care must be taken with online updates to avoid instability.

Drift detection is also used in fraud. Model performance is tracked daily. If there’s a spike in false negatives or a drift in distribution, such as a new distribution in “transaction amount,” they investigate immediately. 

One industry example: A monitoring system detecting that the average transaction amount going to digital gift cards has doubled in the last week could indicate a new fraud trend, such as fraudsters discovering a loophole with gift cards. This triggers a closer look and likely a model update focusing on that feature.

Model drift is expected as the opponent adapts. Companies that excel in fraud detection have robust processes to continuously gather new fraud data, retrain models frequently or even daily, and deploy those updates quickly. They also keep humans in the loop to handle zero-day exploits that the model hasn’t learned yet. If done right, the model rarely drifts far because it’s never too long before it’s refreshed with the newest knowledge.

Model drift in AdTech (advertising technology)

AdTech is another domain where model drift is a constant concern. Digital advertising uses machine learning models for tasks such as click-through rate (CTR) prediction (will a user click an ad?), conversion prediction (will an impression lead to a sale?), bid optimization, and user segmentation. These models run in a fast-paced environment. User interests change, competitors’ strategies change, and even the platform itself, through a new ad format or policy, changes the game.

A common example is a CTR prediction model used in real-time bidding. The model might be trained on recent data to predict the probability that a user will click on an ad if shown. Now consider what happens when a new trend becomes viral. Suddenly, users are all interested in a product that wasn’t in the training data, and ads related to that product get more clicks. The model, however, doesn’t realize this because it has never seen such a pattern; it underestimates the CTR for those ads. 

That’s concept drift: the relationship between ad content and click likelihood changed. The model’s performance drops; it might bid too low or show less relevant ads, hurting revenue. Only once the model is retrained on data that includes the new trend will it catch up.

Seasonality also plays a big role in advertising. User behavior in the holiday season of November and December is different from the rest of the year. If you trained a model on July data, it will likely drift come Black Friday because consumers suddenly behave differently with more shopping intent and different browsing habits. Savvy AdTech teams plan for this by retraining models ahead of season changes or even maintaining separate models for different seasons.

Data also drifts due to external changes. An example is the shift in data availability from privacy measures such as browser cookie restrictions or Apple’s iOS changes. Models that relied on certain user tracking features had to cope with those features disappearing or aggregating differently, an upstream data change causing drift. If a feature becomes less informative, such as a user tracking ID becoming blank due to opt-outs, the model’s input distribution and predictive power change, and performance degrades until the model is adjusted to not rely on it. 

AdTech platforms handle drift through continuous experimentation and monitoring. They often have A/B testing frameworks running, so if a model starts drifting, its variant’s performance will lag, and they’ll notice quickly. They also use drift metrics. Monitoring feature, model, and actual drift is critical for catching changing user behaviors across distributions. If the system sees that the distribution of predicted CTR versus actual click rates is diverging, that’s a sign of trouble.

AdTech companies often retrain models daily or even during the day. Large online advertisers such as Google and Facebook constantly incorporate fresh data. Additionally, they use feedback loops. For example, if a user didn’t click any ads at all yesterday, that data feeds back to update user interest profiles. The goal is to reduce the lag between behavior change and model update.

From a business standpoint, drift in AdTech means money. If the model drifts and campaigns are not updated, an advertiser might overspend for little return or miss out on valuable impressions. Thus, there is a strong incentive to invest in systems and databases that keep up with high-speed data ingestion and model refitting. This is why low latency data storage and feature retrieval, often using real-time databases and feature stores, are so important in programmatic advertising. They let the system use the most up-to-date data for model decisions, reducing drift. 

In fact, an identity graph or feature store powered by a database such as Aerospike serves the latest user profile attributes in milliseconds to an ad decision engine, so the model’s input data is as fresh as possible for each prediction. Keeping data fresh is one aspect; the other is retraining models frequently enough so models don’t get stale. 

Mitigate model drift with constant data refresh, frequent retraining, and comprehensively monitoring both data and outcomes. The dynamic nature of advertising means that without these practices, a model could become irrelevant in a matter of weeks or even days. Teams that master drift have more efficient ads and better user engagement, which helps them compete better. 

Model drift and Aerospike

ML model drift is an unavoidable reality for production machine learning, but with the right approaches, you can manage it so your AI systems continue to deliver value reliably. Through careful monitoring, regular model updates, and robust data pipelines, organizations stay ahead of drift rather than falling victim to it. This is especially important in fast-moving sectors such as real-time analytics, fraud detection, and AdTech, where data changes rapidly, and the cost of a stale model is high.

Aerospike helps address model drift with the high-performance data infrastructure that real-time AI needs. Aerospike’s real-time data platform handles large data streams with ultra-low latency and strong consistency, which means your models always get fresh, up-to-date information and your applications react quickly as conditions change. 

Many enterprises in fraud detection, advertising, e-commerce, and other fields use Aerospike under the hood. For example, PayPal’s global fraud monitoring and Wayfair’s personalization engine use Aerospike to manage many events and features in real time. A database that ingests and serves data at scale makes it easier to retrain models frequently, update features continuously, and reduce the risk of model drift undermining your AI system. Aerospike helps provide the fast, scalable backbone for the data and feature store layers that keep models accurate.

Keeping your machine learning models accurate over time is a process that requires both good MLOps practices and powerful tools. Aerospike offers the speed and reliability to power that process. If your organization is looking to build drift-resistant, real-time AI systems, visit Aerospike to learn more about how Aerospike’s real-time database helps you deliver consistent ML performance. Don’t let your models drift off course. With the right data infrastructure, keep them on track and driving business value for the long run.