Machine Learning

Monitoring ML Models: 5 Things To Keep An Eye On

Jan 23, 2023

5 min read

Monitoring machine learning models is an essential part of the development process, but it can be easy to overlook specific vital considerations. A checklist helps keep track of everything you need to consider when monitoring your models and can help ensure that your models perform at their best.

Earlier in our publication, we discussed the individual components of your Machine Learning Pipeline in our “Ultimate Guide to MLOps,” where we briefly explored different aspects of development, deployment, and monitoring. However, with the latest boom of open-sourced, heavily deployed models like sound diffusion and ChatGPT, you might want to make your deployment bug-free which can be achieved through good monitoring practices.

This blog post will review a checklist of items to include when monitoring your machine learning models, including performance metrics, data quality, overfitting, concept drift, input data, output results, and infrastructure. By following this checklist, you can help ensure that your models are performing as expected and that you are alerted to potential issues.

Monitoring Machine Learning Workflows: Why Should You Do It?

There are several reasons you should consider monitoring your machine learning workflows:

1. Improve model performance: By monitoring your models, you can identify areas where they are performing poorly and take steps to improve their performance. This can include adjusting model hyperparameters, retraining the model on new data, or incorporating additional features into your model.

2. Avoid costly mistakes: Machine learning models are only as good as the data they are trained on, and if the data is of poor quality or contains errors, it can lead to incorrect or misleading results. By monitoring your models, you can catch these issues before they cause problems in your production environment.

3. Keep track of changes: As you update your machine learning workflows, it is essential to keep track of those changes and monitor their impact on model performance. This can help you understand how changes affect your model and identify unintended consequences.

4. Meet regulatory requirements: In some industries, there may be regulatory requirements for monitoring machine learning models. For example, models may need to be monitored in the financial sector to ensure they comply with fair lending laws.

In summary, monitoring machine learning workflows is important for improving model performance, avoiding costly mistakes, keeping track of changes, and potentially meeting regulatory requirements. By monitoring your models, you can ensure that they perform at their best and provide accurate results.

The Checklist for Monitoring Machine Learning Models

Here is a more detailed checklist for monitoring machine learning models:

1. Performance metrics: Make sure to track key metrics such as accuracy, precision, and recall. You should also consider tracking other metrics such as AUC-ROC, F1 score, and mean squared error. Tracking these metrics over time is essential to identify any trends or changes in performance.

2. Data quality: Ensuring that the data you use to train and evaluate your models is high quality is crucial for good model performance. This includes checking for missing values, outliers, and other issues that could impact model performance. Make sure to audit your data to meet your quality standards regularly.

3. Overfitting: Monitor for overfitting, which occurs when a model is too complex and has learned patterns that do not generalize to new data. Overfitting can be prevented by using techniques such as regularization and early stopping. In addition, you can monitor for overfitting by tracking your model's performance on both training and validation data. For example, if the performance of the training data is much higher than the validation data, it could be a sign of overfitting.

4. Drift: Keep an eye out for concept drift, which occurs when the statistical properties of the data distribution change over time. This can lead to decreased model performance and may require you to retrain your model. You can monitor for drift by tracking your model's performance over time and retraining your model as needed when you see a significant decrease in performance.

5. Input data: Make sure that the data you are using as input to your model is formatted correctly and meets the expectations of your model. This includes checking for correct data types, ensuring that all required fields are present, and verifying that the data is clean and free of errors.

6. Output results: Monitor your model's output to ensure that it makes reasonable predictions. This can include checking for unexpected spikes or dips in performance and looking for patterns in the model’s errors. You can also use human evaluation to assess the quality of your model's predictions.

7. Infrastructure: Keep an eye on your infrastructure to ensure it is properly configured and running smoothly. This can include monitoring resource usage, keeping an eye on log files, and monitoring for errors. In addition, make sure to test your infrastructure regularly to ensure that it is functioning as expected.

By following this detailed checklist, you can help ensure that your machine-learning models are performing at their best and that you are alerted to any issues that may arise.


In conclusion, effectively monitoring machine learning models is crucial for ensuring that they operate at their best and provide reliable results. While tracking key performance metrics such as accuracy, precision, and recall is essential, there are other factors to consider. Finally, keep an eye on your infrastructure to ensure it is properly configured and running smoothly. By following a comprehensive monitoring checklist and regularly reviewing the performance of your models, you can ensure that they are operating at their full potential and providing valuable insights for your business or organization.

Written By

Aryan Kargwal

Data Evangelist

Copyright © 2023 NimbleBox, Inc.