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You should never neglect to monitor your machine-learning models

Machine Learning Model Risks

Machine learning has emerged as a powerful tool for organizations across industries to enhance their operational efficiency and make data-driven decisions.

With the increasing reliance of businesses on machine learning models, it is crucial to guarantee their performance as expected. At this point, monitoring the machine learning models comes into play.

To put it simply, the process of monitoring a machine learning model involves the ongoing evaluation of its performance. It involves collecting data on the model’s output, comparing it with the expected results, and identifying any discrepancies.

The main objective of monitoring is to confirm that the model is functioning as planned and to detect and resolve any potential problems.

However, despite its importance, many organizations tend to neglect monitoring their machine-learning models.

In this blog, let’s discuss why you should never make this mistake and why monitoring such models is crucial for your business.

Machine learning models are susceptible to drift

The primary reason for monitoring these models is their susceptibility to drift. Data drift occurs when the data on which the model was trained and the data it is currently being tested on are not the same. This can lead to a drop in the accuracy of the model, and in some cases, render it useless.

By monitoring the performance of a model, you can detect data drift early and take corrective action. This ensures that your model remains accurate and reliable, and continues to deliver the expected results.

Early detection of errors

Another critical reason to monitor ML models is to detect errors early on. Even the smallest errors in a ML model’s code or data can cause significant problems down the line. Monitoring allows you to detect these errors in real time, allowing you to fix them before they cause any significant issues.

By monitoring such models, you can identify potential errors before they become severe, saving your organization time and money in the long run.

Regulatory compliance

Regulatory compliance is a significant concern for businesses across industries. Regulation non-compliance can lead to costly penalties, legal action, and reputational harm. Various industries, such as healthcare and finance, have strict regulations governing the use of machine learning models.

By monitoring your ML models, you can ensure their adherence to the applicable regulations. It allows you to detect and address any issues that may arise, ensuring that your organization remains in compliance with the rules and regulations governing its operations.

Enhanced model performance

The monitoring of machine learning models can lead to a continuous improvement of their performance. By collecting data on the model’s performance, you can identify patterns and trends that can help you fine-tune the model’s parameters and improve its accuracy.

Frequent monitoring can additionally enable you to spot chances for optimizing your model’s performance using new technologies or techniques. This can assist your organization in staying ahead of the competition and attaining a competitive edge in your industry.

Better decision-making

Finally, monitoring ML-based models can lead to better decision-making. They play a critical role in many organizations’ decision-making processes. By monitoring these models, you can ensure that the decisions being made are based on accurate and reliable data.

Monitoring allows you to identify potential errors or biases in the data, ensuring that the decisions being made are unbiased and objective.

What Makes Machine Learning Monitoring Different from Other Methods?

The technique of monitoring machine learning involves the ongoing analysis of data, ensuring the proper functioning of ML models.

This method of monitoring differs from traditional monitoring methods in several key ways:

Continuous monitoring: Traditional monitoring methods are typically performed at specific intervals, such as daily or weekly. Machine learning monitoring, on the other hand, involves continuous monitoring of ML models in real time. This allows for rapid identification and resolution of any issues that may arise.

Proactive identification of issues: With traditional monitoring methods, issues are often identified after they have already occurred. ML monitoring, however, is proactive in nature and can identify potential issues before they become major problems. This allows for proactive intervention to prevent issues from occurring.

Automation: It is highly automated, using advanced algorithms and machine learning models to detect anomalies and deviations from expected behavior. This reduces the need for manual monitoring and allows for rapid identification of issues.

Scalability: The monitoring of ML-based systems is considerably scalable, facilitating the monitoring of extensive datasets and systems. This makes it particularly suitable for organizations that require the monitoring of complex systems with vast amounts of data.

Predictive analytics: The identification of patterns and trends in data, through the use of predictive analytics, can help identify potential issues. This allows for proactive intervention to prevent issues from occurring.

Customization: It is customized to meet the specific needs of different organizations and industries. This allows for tailored monitoring solutions that address specific challenges and requirements.

By leveraging the advantages of machine learning monitoring, organizations can gain greater insights into their data and achieve better results from their ML-based models.

Best Practices for Effective Machine Learning Model Monitoring

Here are some best practices for effective machine learning model monitoring:

  • Set distinct performance metrics and monitor them on a regular basis
  • Continuously track and monitor data quality and model inputs
  • Set up alerts to notify stakeholders when models fall outside of expected ranges
  • Regularly review and update models to ensure they remain accurate and relevant
  • Implement robust testing and validation processes to catch errors and biases
  • Document all changes and updates made to models for transparency and accountability
  • Foster a culture of ongoing learning and improvement around model monitoring and management


Neglecting to monitor your machine learning models can have serious consequences for your organization, including decreased accuracy, increased bias, and costly errors. Monitoring these models is essential for any organization that relies on them for its operations. Remember, monitoring your ML-based model is an ongoing process that requires attention and effort, but the benefits are well worth it in the long run. Therefore, it is critical to ensure that you are monitoring your models regularly to ensure their continued performance and success.