The only thing constant in life is change. Unfortunately, machine learning projects do not end once you have put your machine learning model into production. Machine learning models need to be constantly monitored and tuned to protect against things like concept drift. The fact of the matter is that your machine learning models depend most on how well you are able to tune your data to your model. As with anything in life, change happens. Data drifting is something that can happen when building your models.
As your machine learning models enter production, they stop working as accurately as before for various reasons. Concept drift into one, but you also have other factors like training skew, which is one of the many challenges that people go through comes to machine learning monitoring. It is critical to understand how to optimize your machine learning models while they are in production to keep working as accurately as they did before.
Data Drift and Training Skew
Data drift machine learning models can have terrible results for your program. The main problem with concept drift and data drift is that these things result in your machine learning models losing accuracy. You end up in a situation where your model is now making predictions based on data that has changed. Ultimately, that’s the main issue you deal with when using your machine learning models in production. Nothing in life stays the same, and your production data is no different.
You eventually get to a point where the data has drifted enough that the machine learning models need to be retrained. Concept drift machine learning problems also have the same signature as this. The other part of it is that your training data might vary slightly from the data you use in production. That makes your machine learning models less accurate in certain conditions.
As mentioned above, concept drift is an important problem you need to fix before it causes even more problems for your machine learning models. When it comes to concept drift vs data drift, it’s important to remember that these concepts are more similar than they are different. Concept drift and data drift both have to do about the fact that the model that the data learned from is different.
Concept drift has to do with the fact that the patterns that the machine learning model learned don’t apply anymore. You can think of patterns that typically hold over time but eventually start to break down. For example, shopping data was about constant until the COVID pandemic changed everything. You cannot consider shopping data to be relevant if it includes COVID years. Things like this make the landscape more difficult to predict.
Working When You Have Drift Problems
Ultimately, machine learning models will have to figure out how to deal with concept drift and data drift. You will have a hard time building a machine learning model without these two issues. However, the main thing you can do is to have effective machine learning model monitoring.
Machine learning model monitoring ensures that you measure the effectiveness of your machine learning models. You can detect concept and data drift relatively easily. It allows you to find problems and solve them before they become a bigger part of the puzzle. Machine learning model monitoring ensures that you can be confident your model will work and stand the test of time.