According to The Institute for Ethical AI & Machine Learning, 42% of ML projects do not have monitoring in
place (see Figure 6-1). Even
though that figure is better than the one from the year before (50% with no monitoring), it shows how immature most
companies are when it comes to MLOps.
But monitoring isn’t a “nice-to-have.” Without it, models fail silently. Predictions degrade. Bias can increase
over time. Infrastructure costs can spiral. And perhaps most damaging of all, stakeholders will lose trust in the
system when performance suddenly drops and no one knows why. In regulated industries, lack of monitoring can even
create legal and compliance risk.
By the end of this chapter, you will understand all of the following:
The different layers of ML monitoring
How to design a practical monitoring strategy
What metrics actually matter in production
How to set alerts on aspects that matter
How monitoring connects to retraining and continuous improvement
In short, this chapter will move you from “Model deployed” to “Model managed.”
What to Monitor
Monitoring requirements for ML projects are highly use-case dependent. While generic software metrics are important, ML-specific monitoring focuses on the model’s relationship with changing data and its ultimate ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month, and much more.
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.
Julian F.
Head of Cybersecurity
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.
Addison B.
Field Engineer
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.
Amir M.
Data Platform Tech Lead
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.
