With the work involved to deploy a successful AI or machine learning model, you might think that it is the end of everything. However, your ML models don’t just work perfectly in production. It is why AI explainability and AI monitoring are two big pieces of the puzzle.
Would you fly a plane without any instruments? You have probably answered no, but many companies are doing that when it comes to deploying ML models. They deploy the model and then think that ML monitoring is not a real thing. The result is exactly what you think would happen if you try to fly blind. You end up in a situation where you start losing control of your ML model. You stop understanding what is going on, meaning that your project becomes useless.
What Feedback Do You Get?
The real secret to successful ML models is being able to build a production system that you can monitor why it is working. With ML monitoring, you can start to solve problems as they come up in your production environment.
Because AI isn’t always the easiest thing to operationalize, companies have to start looking to other methods of doing AI monitoring correctly. If they are not doing this, the model eventually degrades, leading to a loss in performance. Like with anything in life, reality slowly shifts beneath your feet. The data you started with might become stale as your program runs for longer periods of time. Having the ability to monitor everything leads to you building a successful workflow that can change as needed.
Traditional Monitoring Solutions Can’t Help
One of the reasons why monitoring is so vital is that your ML models might be tied into important business metrics. You might not know you have a problem until it is too late. Because of this, you need to have a good understanding of how everything works to get the best results possible. There is a feedback loop that traditional AI monitoring has. However, these monitoring solutions rely on metrics that appear downstream in the business cycle. The way you correct that is to focus on upstream model performance issues that can be solved before it starts to affect your business. AI explainability is a crucial component in helping to understand what is needed to make this process work. You need this because AI explainability has to do with knowing how the model works internally.
The big thing with explainable monitoring is having a good understanding of how ML models work internally. When your model works well internally and you can understand everything, it becomes easier for you to figure out whether it is performing as it should or not in production. You want a system that is comprehensive, pluggable, and actionable. It should be comprehensive in that it looks at all factors pertaining to your model performance. It shouldn’t just look at certain things that don’t make a difference. Once that is worked out, you are in a much better place with how your ML models can work with the power of AI explainability and ML monitoring. An integrated solution like xpresso.ai is crucial to helping you solve problems with your ML models.