Best Practices For Machine Learning Model Monitoring

Model monitoring is the process of regularly evaluating, tracking, and auditing machine learning models. This process helps data science and machine learning teams identify any issues with their models and take appropriate action to address them. Through model monitoring, teams can ensure that their models are functioning optimally and meeting the needs of their users and stakeholders.

The practice of monitoring ML model performance is crucial in the transition towards more reliable and unbiased AI systems. Monitoring ML models in both training and production allows for control over the product, early detection of issues, and immediate intervention when necessary. The team will be notified if the data pipeline breaks, a certain feature is unavailable, or the model needs to be retrained. Continuous evaluation of ML model performance provides peace of mind by ensuring the model operates as expected.

What are some of the best model monitoring practices?

Understanding business context

Individuals need to understand business context when making decisions about their ML models to ensure they are aligned with business goals and priorities. Without this understanding, they may make choices that do not meet stakeholder needs. Individuals should coordinate with business stakeholders to gather information about their objectives, desired metrics, and desired outcomes, which can be used to influence the model development process.

Monitoring model performance

The performance of a machine learning model may change as the data changes over time, a phenomenon known as model drift. If the model’s performance is regularly monitored, it may be easier to identify these changes and take corrective action. 

One option to monitor the model’s performance is splitting the data into training and testing sets and tracking the model’s accuracy on the test set over time. Another method is to regularly evaluate the model on a holdout dataset to ensure it performs as expected and identify any issues with the training process or data. Cross-validation, which involves dividing the data into multiple subsets and training the model on each subset, is another option, though it is more computationally intensive. This approach can provide a more accurate estimate of the model’s true performance.

Monitoring data quality

Ensuring that the data used to train and test the model is high quality is essential for optimal model performance. Regularly monitoring the data for missing values or anomalies can help ensure that the model operates on clean and accurate data.

It is also crucial to have a diverse set of data for monitoring. If all the data is from the same source, it may not be possible to detect problems that only occur in certain data types. For example, if a model is only monitored using data from North America, issues that only occur in data from Europe may go undetected.

Using a combination of techniques for monitoring

Different monitoring techniques will identify various types of issues. For instance, a drift detection algorithm will only detect changes in the data distribution, while a rule-based approach can identify outliers. By using a range of techniques, it is more likely that more problems with the model will be detected. It is also crucial to consider that different models will need different approaches, so it is essential to customize the monitoring method accordingly.

Monitoring the model’s input and output

It is important to monitor the input and output of the ML model to ensure that it is receiving the correct input and producing the expected output. This can help identify any issues with the model or the data it is processing.

Setting up alerts

To identify potential issues with the ML model as soon as possible, it is helpful to set up model monitoring alerts that notify teams when the model exhibits unusual behavior, such as a sudden drop in performance or an increase in errors.

Documenting the monitoring process

Maintaining documentation of the monitoring process ensures its repeatability and reliability by MLOps teams. It also enables the sharing of the process with others, facilitates collaboration, and builds trust in the process. Additionally, documentation allows tracking and continually improving the process by updating it as new issues or opportunities for improvement are identified.

Automating wherever possible

Automated monitoring can detect drifts earlier, allowing teams to take corrective action before the model’s performance suffers. There are several ways to automate model monitoring, from open source model monitoring tools, such as TensorFlow Model Analysis or Apache MXNet Model Server, to enterprise tools used by dedicated teams. Regardless of automation method, it is critical to leverage both monitoring and explainable AI functionality to identify the root cause of issues detected.

Keeping stakeholders informed

If a model does not function as intended, it can have serious consequences for the business. For example, suppose a fraud detection model begins to produce a large number of false positives. In that case, legitimate transactions may be blocked, causing customer frustration and financial losses for the company. Therefore, stakeholders must be informed of the performance of the models they are responsible for, so they can detect and address any problems.


Effective ML model monitoring is crucial for the performance and reliability of machine learning models. Best practices such as monitoring performance, setting up alerts, evaluating performance on multiple datasets, and monitoring input and output enable stakeholders to identify and address issues with their models and ensure they function as intended. These practices help businesses maximize the value of their ML models, reduce risks, and build responsible AI.

Note: Thanks to the Fiddler AI team for the thought leadership/ Educational article above. Fiddler AI has supported and sponsored this Content.

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