If you are a business with machine learning models in production, you must have a monitoring strategy that is both coherent and effective. If you don’t, you might end up in a situation where your machine learning model does you more harm than good. That is due to the natural drift that all machine learning models go through. The model eventually becomes out of date, and you might not know that has happened if you don’t have a model monitoring strategy.
When your ML models in production start having data integrity problems, you end up with software that produces erroneous results. Even worse, your software could fail altogether. This is a major problem because the machine learning models are often black boxes to the software engineers who implement them. They are usually the purview of the data scientists who created them in the first place. They pass them over to the software engineers for implementation, but it doesn’t mean the software engineer understands the inner workings of these algorithms.
Potential Things That Can Go Wrong
Many production problems can happen to your machine learning models. Data drift is the easiest ones, and it often happens for multiple reasons. Data drift is the slow process by which the parameters you have set for your machine learning model slowly drift because the data has changed. For example, maybe you train your algorithm on competitors, and now they have changed. This changing data set produces problems if you don’t retrain the model to account for the changes. It is one of the many reasons why model monitoring has become such a crucial component of the machine learning development process.
Concept drift can also happen, which is the features and the target slowly change. As mentioned above, the data used to train your model might not be the same as the data used in production. If this mismatch occurs, you will probably start seeing problems with data inaccuracy. It is something that causes problems for ML models in production.
Model Degradation and Drifting
Most production machine learning models have data sources that are pretty diverse. This is a significant factor that has to be monitored to prevent model degradation and drifting. You will eventually encounter data integrity problems in production, making it even more important to have a monitoring strategy ready for your business.
This uncertainty in production environments is one of the many reasons companies should start emphasizing monitoring their machine learning models. If a company is monitoring its model, these types of problems become a simple fix. However, if they don’t, it might become a major problem that causes the application to work badly.
Production Issues That Are Solved by Controlling Your Model
Most production and development environments are not the same. One of the aims of MLOps has been to close this gap, but it will take a while for that to happen. A simple configuration difference between your production and development environment could lead to trouble for your model.
The solution to all the problems mentioned above is to have proper control over your model. You need to have a monitoring strategy that lets you know if:
• Data degradation has occurred
• Data sources haven’t changed
• Production resources are sufficient to meet the demands placed on it
• Model performance is remaining consistent
While these are not easy implementations, they will become an ever-increasing part of what MLOps and running machine learning models in production have to solve.