As machine learning (ML) and artificial intelligence (AI) become more ubiquitous, more and more companies have to put into place model monitoring procedures and data observability practices. But what do these terms mean? How are they similar or different?
Monitoring and observability are two terms that are frequently used when talking about machine learning and artificial intelligence. Let’s dive into these two concepts to understand how they are similar and different.
“Observability” often refers to the statistics, performance data, and metrics from the entire machine learning lifecycle. Think of observability as a window into your data—without the ability to view the inputs, you won’t understand why you’re getting the outputs you receive. Once you have observability, your MLOps team will have actionable insights to improve your models.
Model monitoring is a term to describe the close tracking of the ML models with the goal of identifying potential issues and correcting them before poor model performance impacts the business. This monitoring can occur in any stage in the model life cycle, from identifying model bias in the early stages to spotting data drift after the model is implemented. One way to think about model monitoring is like a coach for an athlete. As the athlete is practicing, the coach notes how the athlete is performing and gives immediate feedback for corrections and tweaks to improve the overall output.
You may be wondering, is observability the same as monitoring? These terms are often used interchangeably due to their similarities. In fact, when discussing observability vs. monitoring, the shades of difference don’t particularly matter—both terms encompass the same goal: better models.
The MLOps lifecycle typically has nine stages:
As you can see, model monitoring is built into the last stage of the MLOps lifecycle and data observability is vital for every step of the way. Without this view into your data and close scrutiny of your models, your AI quickly becomes a black box with minimal insight into its operation. Observability and monitoring are key to explainable AI, making your black box more like a glass-box so you can always understand the ‘why’ behind model decisions.
Observability and monitoring tools for AI can come in many different forms. From dashboards to platforms, there are several ways you can attempt to build observability and monitoring into your MLOps process. One example is our model performance management platform. We have extended traditional model monitoring to provide in-depth model insights and actionable steps, giving detailed explanations and model analytics. Users can easily review real-time monitored output and dig into the “why” and “how” behind model behavior.
Request a demo to better understand how continuous model monitoring and explainable AI can help: