ML Model Monitoring Techniques and Strategies

Every ML model developed with a purpose needs to conform to the initial concept and desired performance benchmarks in order to be effective. To that end, it is necessary to monitor the ML model performance so as to identify data drift and model degradation to help maintain optimum performance at all times.

After the ML model is deployed, enterprises must consider ML model monitoring to ensure that the quality of output remains within the tolerance limits and doesn’t degrade with time.

Especially where automated decision-making and other critical business operations are involved, ML models must be monitored to prevent deviations occurring because of changes in feature inputs or data divergence.

Significance of Monitoring ML Model Performance

ML model monitoring enhances the visibility of the performance of the deployed model. Especially in the cases where ML models conduct predictions and forecasts, deviation beyond tolerance levels can result in miscalculations and errors in business decisions.

ML models base the improvement in performance on base data that is fed into them. The main goal of monitoring ML model performance thus revolves around troubleshooting for bad data and sub-par performance. It is a way to enable data scientists to generate reliable predictions and forecasts without losing control of the process and also maintain a certain level of predictability in the workflow.

A good example of ML model monitoring is setting up automated alerts when the output deviates further than the tolerance limits for outliers. These alerts help data scientists to apply troubleshooting early in the process to prevent printing a permanent impact on the outcomes and further the learning process of the ML model.

Key Metrics for Machine Learning Model Performance

You can understand ML model monitoring data with the help of the following metrics:

1. Accuracy and Precision

Accuracy is an ML model metric that highlights how many times the ML model produced correct or accurate results. For example, if out of 100 iterations, your ML model predicted the correct output 56 times, the accuracy of your model is 56%.

Prediction is the metric that highlights how good your ML model is at predicting specific items.

2. Recall and F1 Score

Recall is an ML model metric that you can use to understand how correctly your ML model identifies the case in question. It can be understood better with the following Confusion Matrix:

F1 Score is a corollary mathematic metric that highlights how best your ML model is able to select the right prediction. It is a harmonic mean of recall and precision.

3. ROC-AUC Curve

AUC – ROC stands for Area Under the ROC Curve. It is a graphical plot that helps data scientists understand the True Positive Rates of their ML models measured against the False Positive Rates. It is a way to measure the specificity and sensitivity of your ML model.

ML Model Monitoring Techniques

The five key strategies for ML model performance monitoring are:

1. Monitoring Data Drift

Data drift is the phenomenon where the input data to an ML model changes over time, negatively impacting the output into degradation. Some of the key metrics data scientists use to identify data drift and launch corrections are:

• Population Stability Index (PSI)
• Jensen-Shannon Distance: Measures the bounded variant of the relative entropy of the system
• Kolmogorov-Smirnov Test: A statistical test to monitor changes in distributions

Monitoring data drift helps maintain model accuracy.

2. Model Interpretability and Explainability

Interpretability is the measure of how easily a data scientist can understand the basis of an ML model’s predictions and results. It is key to debugging, auditing, and finetuning your ML model.

The explainability of an ML model involves basing the model’s results in human terms and actions to understand the results better. It helps make the ML model more transparent.

3. Establishing Monitoring Benchmarks

Every ML model is expected to deliver results as close to accurate as possible based on the data inputs. Data scientists should, therefore, establish clear monitoring benchmarks that create a range of tolerances for deviations.

Creating a dedicated protocol for encountering erroneous or outlying results helps with preventing data drift and maintaining model quality.

4. Continuous Validation

Continuous Validation (CV) refers to employing a set of tools and processes that monitor the performance of your ML model in real-time right from the point of deployment. Using CV, data scientists are able to mitigate major risks occurring because of problematic models and validate the correctness of their machine learning systems.

It also helps to validate the accuracy of all the statistical models involved.

5. Feature Importance Tracking

Feature importance is a ranking system that helps data scientists determine the significance of input features to the predictions that a model creates. It is helpful in understanding which features have the highest importance in an ML model and, therefore, is capable of creating a bigger influence on the results it generates.

Feature importance can be used to measure overall model predictions or for specific samples.

Machine Learning Model Monitoring Performance Best Practices

The following best practices can help you optimize the performance of your ML model:

1. Establishing a Monitoring Framework

An ML model monitoring framework involves creating the wireframe of processes, tools, metrics, and methods that help data scientists enhance the visibility of the performance. The framework establishes a protocol for eventualities and guides the process flow in case changes or tweaks are required.

It works as a reference to understand model behavior.

2. Retraining Strategies

ML models generate predictions and results based on the training data inputs. Creating retraining strategies is an excellent way to fall back on when the model is consistently generating inaccurate or highly deviant results.

It involves defining how to take a step back and reassess the model’s condition to retrain it.

3. Addressing Bias and Fairness

Not all data that goes into an ML model is neutral and fair. It should be standard practice to address data biases, fairness, and neutrality of the input features to ensure that the ML model works with facts, not opinions.

It impacts the accuracy and precision of the generated results tremendously.

Conclusion

Critical business functions today rely heavily on accurate predictions and forecasts to make important decisions. ML model monitoring is of pivotal importance to streamline the accuracy and performance of the deployed models to enable empowered business decisions.

MarkovML aids businesses in simplifying their data analysis processes by providing robust data intelligence and management features. For example, with MarkovML, your enterprise can leverage no-code data analysis to uncover deep insights. Several other features of the platform, like collaborative reporting and generative AI, provide key capabilities to enterprises in reinforcing their operations.

Schedule a call with MarkovML today for an in-depth insight into how AI-based platforms can benefit your business.