Introduction to Model Monitoring
Building a good machine learning model and putting it into production is only the beginning of your project. Model monitoring is a typical step that most people don’t think about or discuss until it’s too late. However, ML model monitoring is one of the most important pieces of the puzzle. You cannot have a successful machine learning initiative without ML monitoring. Monitoring models in production is so vital because there are so many things that can go wrong and cause problems. Machine learning projects are not the same as software engineering ones.
In software engineering, almost nothing will go wrong if your environment does not change. Your software code will continue to work normally for decades without any problems. What is model monitoring in this context? Model monitoring involves constantly checking the parameters of your machine learning model to ensure that it continues to deliver accurate results over time. The reason why model monitoring is vital is that there are so many things that can change your model for the worse. These problems can cause your model to get inaccurate.
Model Monitoring in the ML Lifecycle
Model monitoring is typically the last phase of the machine learning development lifecycle. It is the step where you actually start putting your model into action. You can achieve great results by ensuring that your machine learning model continues to work accurately without any problems. What are some problems that can happen to your machine learning model?
The main issues have to do with the data fed into your model. As you may remember, machine learning models depend on being fed data in order to get results. If the data is different in production, you can be sure that the results will not be the same as when you trained your machine learning model. This is where ML model monitoring comes into play. The key to ML monitoring is ensuring that the model is constantly making accurate predictions. You also want to ensure that there are no significant changes in the data that could result in inaccurate predictions.
Doing Model Monitoring Right
The most important thing about model monitoring is ensuring you have the right tools to do it well. Model monitoring tools are critical in this aspect, but you have to choose the ones that will look at the correct parameters. The most important part of the process is finding model monitoring tools that are easy to use and understand. You also want to ensure that the tools won’t generate false errors every so often. However, how do you do ML monitoring the right way? The answer to that has to do with figuring out whether your model is drifting and for what reason.
You have concept and data drift as two options that could happen. You also have the problem of real-world reality shifting very quickly. An example of this is when prices went negative during Covid. The same thing can be said of airline tickets as well. Because of that, you must understand whether your model is performing well or not.
A Sample ML Monitoring Tool
As mentioned above, the best model monitoring tools are the ones that are easy to use and configure. These are the tools that will continue to provide you with accurate results over a long time without any problems. Once you understand what is going on, these ML monitoring tools will also be valuable for you in correcting any mistakes that might come up.
It is quite difficult to figure out why your model started to become inaccurate. However, finding a good ML monitoring tool is critical to that reality. That is one of the many reasons why xpresso.ai is the right choice for the entire machine learning model development lifecycle.