It is often said that change is the only constant in life. Unfortunately, in the machine learning industry, this rings out a lot truer than we would like to admit. We would all love to create a machine learning model that we never have to touch again. Unfortunately, data drift and concept drift are two phenomena that can happen in the industry.
Data drift machine learning issues eventually pop up in production for a whole variety of reasons. The same is true for concept drift machine learning problems. These two classes of problems are crucial, and that is why you need to understand what they are and how they compare to each other. Understanding is the first step because you acknowledge that there is a problem that can be solved with input from you.
Data Drift vs. Concept Drift
The concept drift vs data drift debate is an important one to understand in the context of building better machine learning models. However, when you get down to the fundamental core of it all, the concept drift vs data drift debate is easy to understand. That is because data drift and concept drift are two well-defined phrases for things that happen in machine learning projects that we all know and understand without needing technical knowledge.
The big issue with data drift is that it simply means your model isn’t relevant to the data in production. What does that mean? It means that your data has changed, meaning that your model can no longer make accurate predictions. When you train a machine learning model, it is tuned to the data you trained it with. If that data changes in production, the machine learning model stops being accurate. Concept drift is simply when there is a change in the relationship between the input and output data in the underlying machine learning problem. The classic example is spammers changing tactics rendering your machine learning model less accuate over time.
Data Drift and Model Monitoring
The biggest thing you can do to fix data drift is model monitoring. Model monitoring involves analyzing your production workloads to ensure that the data hasn’t shifted to a degree that would cause your machine learning model to become inaccurate. When data drift occurs, model monitoring allows you to spot that drift much faster than if you weren’t monitoring at all.
Data drift is an inevitability, and you must monitor your machine learning models to ensure you fix the problem before it becomes even bigger for your application. Model monitoring also allows you to track performance over time, allowing you to know when to precisely raise the red flags for changes to occur.
Why Data Drift Matters
It might not seem crucial, but data drift and concept drift are two problems you should always think about when deploying machine learning models. They matter a lot because they underpin a successful machine learning initiative. The biggest issue is that data drift inevitably leads to you having to stop your application to fix the model.
If you don’t catch problems early with model monitoring, data drift also means your model will underperform for a significant portion of the time. That underperformance might cost you money if you aren’t careful. Ultimately, it is crucial to understand how data drift and concept drift matter in the paradigm of successful machine learning projects.