Training and deploying ML models is relatively fast and cheap. But operationalization, the work of maintaining, monitoring, and governing models over time, is difficult and expensive. Explainable monitoring extends traditional monitoring to provide deep model insights with actionable steps.
The Shift to Operationalization
The rise of MLOps represents a shift toward operations. A significant portion of the fastest-growing open source projects now concern ML infrastructure, tooling, and operations. These innovations respond to organizations trying, and often struggling, to get their ML projects out of the lab.
Traditional engineering processes are based around software that teams write, test, and deploy. While software might be A/B tested for effectiveness, the software itself is not changing. Machine learning is different. Models change as data changes. Monitoring and explainability are therefore key components of successful AI systems.
The COVID-19 Lesson
When COVID-19 hit, many companies saw timeseries data that looked normal until suddenly it did not. If you do not have a way to recognize when the macro environment has shifted, you will have problems.
Airlines experienced this dramatically. At the start of the pandemic, their pricing algorithms dropped prices dramatically because they mistakenly thought lower prices would get people flying again. Many companies had to rapidly retrain models as they saw sudden drops in some metrics and surges in others.
This is not just about pandemics. Any significant shift in the business environment can cause models to behave in unexpected ways. Without monitoring and the ability to understand why models are changing, teams fly blind.
Why Traditional Monitoring Is Not Enough
You have to assume that things will go wrong and your machine learning team will be under the gun to fix problems quickly. If you have a model you cannot interrogate, where you cannot determine why accuracy is dropping, that is a stressful situation.
This is even more important for high-stakes use cases involving fairness and vulnerable groups. Debugging models is something that is still developing. We do not have industry-standard tools for this like we have for traditional software.
A lot of current practice is manual and ad hoc. Notebooks flying around in emails. We need benchmarks that we look at consistently and continuously.
The Power of Combining Monitoring and Explainability
ML monitoring and the ability to drill down and explain are inextricably linked. When you have both, you get faster detection and resolution of issues. At the same time, ML engineers develop better intuition about which models and features need more work.
Consider what happens without explainability. You detect that model accuracy has dropped. You do not know why. You start investigating: is it data quality? Distribution shift? A specific subgroup? Without explanations, this investigation is slow and frustrating.
With explainable monitoring, you can see that feature importance patterns have changed. You can identify which features are now contributing differently to predictions. You can pinpoint whether the change affects all predictions or specific segments.
Multiple Stakeholders Need Visibility
Different personas care about models and their outputs.
Data scientists and engineers need technical details for debugging and improvement.
Product managers care about fit with business strategy and purpose.
Legal teams and regulators require access to information for compliance.
End users may need explanations for individual predictions.
C-suite leadership wants to know how models are performing at a high level.
Having observability and monitoring provides a shared understanding of the levers and trade-offs. Having a conversation at that level of abstraction goes a long way toward building trust and enabling collaboration.
For people who do not understand what data science teams do day to day, the whole team can feel like a black box. Monitoring and explainability give them something to look at where they can see progress and understand what is happening.
Preventing Bias Through Continuous Monitoring
One of the most important use cases for explainable AI and monitoring is preventing issues with bias and fairness. Unwanted consequences can creep in at any part of the pipeline. Organizations must think about it holistically, from design to development, and should have continuous monitoring for bias and fairness.
Continuous monitoring helps teams "trust but verify." With many people working asynchronously to improve collective performance of an AI system, individual bias can creep in over time even though no single person is controlling how the system behaves at the macro level.
Explainable monitoring can surface these emerging issues before they become serious problems.
Building for Operations
The trend in AI tooling is toward a heterogeneous, best-in-breed approach that combines open source, custom software, and vendor solutions rather than one tool that does everything.
The more valuable and important the project, the more you want the best component for each piece. In traditional software, that means combining different solutions for CI/CD, testing, monitoring, and observability. The same logic applies for ML.
You cannot build an end-to-end solution and expect to succeed in an industry evolving so quickly. You need to be able to switch out parts while you are running. Components that were popular two years ago may not be the best choices today.
For companies that are serious from the start, best-of-breed solutions for monitoring and explainability will be their competitive advantage.
From Monitoring to Action
The goal of explainable monitoring is not just visibility but action. When you detect an issue, you need to know what to do about it.
Should you retrain the model? Balance the dataset? Continuously monitor and use insights to adjust the application? The answers depend on understanding what is happening and why.
AI observability without explainability tells you something is wrong. Explainability without monitoring tells you how individual predictions work but not how the model is behaving overall. Together they enable informed action.
The organizations that operationalize AI successfully are those that build both capabilities from the start, not as afterthoughts when problems emerge.
