Definition
Model monitoring is the process of systematically observing, assessing, and measuring machine learning models after release to guarantee they maintain expected performance in real-world environments. The process involves monitoring both technical and business metrics to in other to identify changes in accuracy, data quality, prediction behavior, and system stability over time. The goal is to detect when a model’s outputs begin to deviate from expected performance or when its decisions begin to negatively affect business operations [1].
For instance,
imagine everyday payroll monitoring system of companies and Federal MDAs. As
employees and payroll officers deal with ghost workers and inflated salary
claims, so must our models develop to know the difference between legitimate
payroll entries and increasingly cunning fraudulent transactions. It’s a never-ending effort, and without monitoring,
our model's performance could diminish as insiders develop sophisticated
techniques to embezzle company funds through manipulated payroll systems.
A model
monitoring process [2].
Origin
The origin of Model
monitoring can be trace back to the 1990s when installed machine learning
systems in banking and fraud detection experienced performance degradation over
time, known as "model drift." The field gained formal recognition in
the early 2000s through data mining competitions that revealed overfitting
issues, while the 2015 Google paper on ML technical debt and 2018 GDPR
regulations fast tracked the development of monitoring frameworks. Between
2015-2020, major cloud platforms integrated monitoring features and specialized
companies like Arize AI emerged to track data drift, performance metrics, and
fairness. The modern MLOps era (2020s) established comprehensive automated
systems, with the recent rise of Large Language Models (2023-2025) introducing
new monitoring dimensions like hallucination detection and response quality
assessment .
Context and Usage
Model monitoring
is practiced in sectors such as the following;
- Fraud Detection in Finance: In the financial sector, companies constantly monitor fraud detection models, allowing for rapid detection and adaptation to new fraud patterns. This makes sure models maintain accuracy in detecting suspicious transactions.
- Healthcare Predictive Models: AI Model Monitoring is used to track the effectiveness of patient risk assessment models, improving predictive accuracy and resource allocation [3].
Why it Matters
Monitoring is
not a one-off task that you do and forget about. Monitoring a machine learning
model after installment is very important, as models can break and performances
drop in production.
To determine when to update a model in production, there must be a continuous real-time view that allows stakeholders to assess the model’s performance in the live environment. This guarantees that your model is performing as expected. Having as much visibility as possible into your installed model is necessary in other to detect issues and the source before they cause a negative business impact [4].
In Practice
Evidently is a good example of a real-life case study of a platform that offers model monitoring services. Evidently is an open-source Python library that helps you monitor and evaluate machine learning models. It is designed to track the performance of your models during development and also when they are deployed in production [5].
- Model Interpretability: Ability to understand and explain how a model makes decisions
- Model Training: Process of teaching an AI model to make predictions by learning from data
- Model Versioning: Practice of tracking and managing different iterations of AI models over time
- Overfitting: Problem where a model learns training data too well and fails to generalize to new data
- Regularization: Techniques to prevent overfitting and improve model generalization
- Avahi. (2025). Model Monitoring
- Evidently AI Team. (2025). Model monitoring for ML in production: a comprehensive guide
- Lyzr Team. (2024). AI Model Monitoring
- Pykes, K. (2023). A Guide to Monitoring Machine Learning Models in Production
- Anandani, A. (2025). Model Monitoring in Machine Learning Explained with Evidently AI