Best Practices of Model Monitoring


Best Practices of Model Monitoring Team
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One of the biggest problems in the machine learning industry is engineers not being able to see inside their ML models. The machine learning model essentially becomes a black box, making it difficult to understand whether it performs well or not. That is one of the many reasons why model monitoring is such an important part of every MLOps framework. ML model monitoring is a way of helping you look inside the model to see flaws and other errors that can occur when you are building your projects. Once you can do that well, it makes the entire process more streamlined.

ML monitoring tools are becoming more important because engineers now see a need to build out intricate frameworks to ensure that the model runs as it is supposed to. A model that doesn’t run well often leads to incorrect results. Tuning your model’s performance is also more important than ever, with model bias and explainability being major concerns when developing projects.

What Is Model Monitoring?

You can think of model monitoring as a series of efficient processes that you undertake to gain an understanding of how a model is performing. You can think of it as the metrics and analytics you would get from other things. ML model monitoring involves tools and systems that help you look inside your machine learning model to see what it is doing. Model monitoring is how you measure how well your model performs during training and deployment. It involves a process of getting feedback, seeing changes, improving ML models, and using this data to detect drift.

Once you can understand what your machine learning model is doing, you can better measure and classify the metrics you are looking for. It is a great way of helping you gain a deep understanding of why your model is performing the way it does. You can also look for data drift, which happens due to changes in the input data. Model monitoring is especially useful in the early stages of development because it lets you know whether you’re doing something wrong before it becomes a major problem.


Measuring Your Model’s Performance

The big problem is that model monitoring can be complex when measuring how your model performs. There are not many model monitoring framework solutions, making it even more vital to understand the few that are available. ML model monitoring tools are also crucial to understand, but you need to also have a good understanding of why you do the things you do. For example, different types of models will require different solutions. These various model monitoring metrics can be classified according to the following:

  • Classification Metrics
  • Statistical Metrics
  • Regression Metrics
  • Deep Learning Metrics
  • NLP Metrics

Each one needs to be understood to do model monitoring in the smartest way possible.

Doing ML Model Monitoring the Smart Way

The best way to do model monitoring effectively is through an MLOps framework. It provides the necessary tools and processes already, making it easy for you to do all the steps that ML model monitoring requires. However, as with anything in the world, there are many risks to using open-source model monitoring tools. Depending on what you are trying to accomplish, your tool needs to be robust and functional enough to conduct all the various processes you have working for you.

The other option is to create your own model monitoring framework. However, the main downside is that you are pouring resources into something that is quite difficult and time intensive. It makes it possible to get even worse results than finding and tuning an effective model monitoring framework that is already open source.

About the Author Team Enterprise AI/ML Application Lifecycle Management Platform

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