In machine learning space, concept drift comes into the picture when the statistical properties of dependent or target variables or the input variables change with time in a machine learning procedure. The dependent variable of the target variable is a variable from the whole data that which model is trying to predict. In a different way, we can say that concept drift is an impact which occurs when the assumptions made during the model training phase no longer hold true in the operational phase.
In general terms, we can say that concept drift occurs when the relationship between target and independent variables on which any model was trained becomes invalid, or the new relationship becomes unknown to the model. The concept drift causes inaccuracy in the model because in production model doesn’t knows about the changes.
Concept Drift vs Data Drift
As discussed above, the term concept drift is somewhere related to the accuracy of the model, which occurs because of the changes in the data relationship. When we talk about data drift, we find that it is a concept which occurs when the properties underlying any data change drastically or significantly. The below table represents the general difference between these two terms:
Now let’s take a look at how does concept drift looks like.
An Overview of Concept Drift
to understand the term, let’s take the help of an example where we are building a machine learning model to predict stock prices based on historical data. We train a model using the dataset, including information from the past five years and factors like company performance, economic indicators, and market trends. After successfully training the model, the model can make predictions based on learned relationships between data. So we deploy the model in a real-time setting.
After the deployment model starts making predictions on new incoming data. However, after a few months, we start noticing that the model’s performance declines. The predictions become less accurate, and the trading decisions based on those predictions generate suboptimal returns.
Based on the investigation, we realize that the changes in performance accruing because of the concept drift problem. The following can be the reason behind the concept drift:
- Changing market conditions
- Evolving trends and patterns
- Model performance deterioration
As the concept drift occurs, the model’s predictions become less reliable. The previously learned patterns and relationships may no longer capture the current dynamics, leading to suboptimal predictions and trading decisions.
How to Address Concept Drift
There are various ways which can be used to address the problem of concept drift. Also, many of them are use case specific, but here we will take a look at the general ways to address the concept drift. Let’s take a look at them one by one.
- Monitoring: it is equally necessary to investigate the concept drift before addressing it. We can also think of it as addressing the concept drift because here, we regularly track the model’s performance metrics, such as prediction accuracy or trading returns, and detect any significant decline over time. This can serve as an indication of concept drift.
- Data collection and retraining:as we know, concept drift causes the model performance deterioration. We can avoid this by regularly collecting new data and periodically retraining the model to update its knowledge. In the above use case by incorporating the latest market information, the model can adapt to the evolving patterns and maintain its predictive power.
- Ensemble Method: Ensemble methods are a type of machine learning training method where multiple models with different assumptions or algorithms work together. By combining their predictions, the ensemble can account for different aspects of concept drift and provide more robust predictions.
- Feature Selection and Engineering: Feature Selection and Engineering is another way to address concept drift as, using these methods, we can Identify robust and stable features that are less likely to be affected by concept drift. We just need to focus on features that have a stronger relationship with the target variable or have more consistent statistical properties over time. We can also regularly assess and update your feature set to maintain model accuracy.
- Continuous Evaluation: In such a dynamic machine-learning system, there is always a need for a continuous evaluation framework so that model performance is frequently addressed and compared against a predefined threshold. If the performance drops below the threshold, appropriate actions can be taken to address concept drift.
In order to address concept drift problems and enhance machine learning procedures, organizations are increasingly adopting the MLOps trend. This involves the integration of various technology components into their systems to create scalable, flexible, and reliable machine learning systems. If you are also looking for the same here, UnifyAI is a way to do so. Let’s explore how unifyAI can help us to address and prevent concept drift.
Addressing Concept Drift with UnifyAI
Having extensive industry experience has given us valuable insights into the working standards of various industries. It’s not uncommon to encounter concept drift issues when dealing with machine learning models. In today’s dynamic scenarios, there are multiple causes and potential solutions for concept drift problems. However, implementing these solutions at scale presents a significant challenge. Many companies struggle to build systems that can effectively address concept drift and cater to the specific needs of their machine-learning models.
With the aim of making AI available for everyone, DSW | Data Science Wizards has engineered a solution platform UnifyAI that not only helps organisations to take their AI use case from experimentation to production with ease but also ensures the scope of scalability, flexibility and monitoring. On the other hand, it is enabled with such components that can help organisations to reduce the problem of concept drift to its minimum.
UnifyAI combines multiple open-source technology components to effectively address concept drift problems. One of these components is our Monitoring System, which not only alerts users about concept drift but also detects data drift. The Core Engine of UnifyAI is built with a robust design, simplifying the process of periodically retraining the model to keep it up-to-date. This reliable core engine, coupled with the Development and Integration Toolkit, strengthens the system as a whole. This enhanced system enables high scalability and flexibility, empowering processes like ensemble learning to be performed with greater efficiency.
UnifyAI offers an integrated Feature Store that simplifies feature selection and engineering for users. This component enables users to choose robust and stable features that are less susceptible to concept drift, ensuring the safety of their machine-learning procedures. Additionally, UnifyAI includes a user interface with a Performance Matrix Evaluation Framework. This feature allows continuous performance evaluation of models, effectively safeguarding them from the impacts of concept drift.
About Us
DSW, specializing in Artificial Intelligence and Data Science, provides platforms and solutions for leveraging data through AI and advanced analytics. With offices located in Mumbai, India, and Dublin, Ireland, the company serves a broad range of customers across the globe.
Our mission is to democratize AI and Data Science, empowering customers with informed decision-making. Through fostering the AI ecosystem with data-driven, open-source technology solutions, we aim to benefit businesses, customers, and stakeholders and make AI available for everyone.
Our flagship platform ‘UnifyAI’ aims to streamline the data engineering process, provide a unified pipeline, and integrate AI capabilities to support businesses in transitioning from experimentation to full-scale production, ultimately enhancing operational efficiency and driving growth.