It might seem strange, but you are not finished with your machine learning project once your model is in production. What is model monitoring? It is acknowledging the reality that machine learning models can change in production. It is the way you track how your ML models perform, so you can then make adjustments to improve them. There is a robust knowledge base in terms of model monitoring tools.
That is because monitoring models in production is becoming such an important task in the machine learning world. You also need to manage your models for compliance issues and whatever else might pop up in today’s complicated business world. Having a good understanding of how model monitoring works will help you navigate this world successfully. This will also improve the impact that machine learning models have on your business.
How Important Is Model Monitoring?
In explaining what is model monitoring, we also need to look at how model monitoring tools work. This is how you get the choice in a way that you can understand the importance of model monitoring. The reality is that your model degrades the instant you put it into production. From there, it only goes downhill in terms of its predictive power.
Eventually, your model loses all of its predictive power if you don’t change and adjust parameters. The data set you use is bound to change, and the training you do will no longer be relevant in the current business climate. ML monitoring works by ensuring that all of these things are kept in check. The model monitoring tools on the Internet ensure that governance, lifecycle management, and all other things are focused on when it comes to deploying successful machine learning models.
Way to Do Model Monitoring
The main way to do machine learning model monitoring is through a set of tools and practices. Model monitoring is simple on the surface, but it is quite complicated when you get into the nitty-gritty details of how it works. An MLOps library is one of the most important things regarding monitoring models in production.
This is eventually the part of the architecture used to get things done. However, you can use an all-in-one tool like xpresso.ai to give you all of that functionality in one simple-to-use package. This is how model monitoring can be made much more successful without having to worry about how difficult this process is in the real world.
Model Monitoring Architecture
There are three different things you need to think about in the model monitoring world. Model monitoring tools must be part of the overall architecture. This is because monitoring models in production need multiple layers in the machine learning development lifecycle. The first thing you need is the MLOps library, as mentioned above. You then have to think about all the other layers that go into a model having its predictive power.
Model monitoring, therefore, includes all of these puzzle pieces in the overall architecture. Eventually, you start working on monitoring how the database performs and the lower-level application. This leads to a situation where your ML monitoring solution can work exceptionally well.