As with most things in software engineering, you never know how it will perform until you put it into production. Machine learning models are no different, which is why model monitoring is such an important part of the process. What is model monitoring? ML model monitoring helps you understand and quantify how useful your machine learning model has been.
When you do ML monitoring correctly, you can understand the value that your machine learning model brings to the table. Monitoring models in production is a notoriously difficult prospect, which is why you need to understand I it works to implement it correctly. It is always better to do model monitoring than not. That is because doing model monitoring makes your initiative a scientific process.
Why Monitor Model Performance?
If you don’t do model monitoring, then your machine learning project isn’t scientific. You are essentially shooting in the wind and hoping something sticks. There are also many model monitoring tools out there that can help you get the job done quickly and easily. That makes it possible to do model monitoring in a way that will be fast and extremely reliable.
These tools also make monitoring models in production a snap. As a result of all of this, you are able to optimize your model performance to get the most out of your initiative. Monitoring models in production is one of the most important parts of the lifecycle of your project. However, it is a piece of the project that many people don’t think about when trying to implement machine learning initiatives.
Things to Check and Track
If you’re doing ML model monitoring, the next thing to think about is what to check and track. Since machine learning models degrade over time, data distribution shift is one of the big things you should check.
Data drift is a major problem, and it can cause your model to stop working very quickly. There are many model monitoring tools that can help you with this. Performance is also a major concern when it comes to monitoring models. You can check how performance changes over time, which is something that will help you figure out how to optimize performance as you go on.
You can also check and track data integrity, as that will help you do things through the entire lifecycle. It helps you ensure that your data pipeline is as optimized as it could possibly be. You can also check for bias and fairness, which is becoming more important for monitoring models in production. ML monitoring has gotten difficult in terms of data bias because machine learning models are being integrated into the way we live our lives.
Using It to Improve Your Model
Model monitoring is one of the most important pieces of the puzzle to making your machine learning models better. The people and organizations that do model monitoring well are the ones that can optimize over time to get the most out of their initiatives.
They are also the ones that will benefit the most from the improvements that come from the machine learning ecosystem. By doing model monitoring currently, you guarantee even performance and machine learning models that don’t degrade over time.