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Mind Your Models: 5 Ways to Implement ML Monitoring in Production

Machine learning (ML) models are the backbone of modern business operations, enabling unparalleled automation and optimization.  But here's the catch: deploying ML models is just the beginning of the journey. Monitoring their performance in production is essential to ensure they continue to meet the expected outcomes. In this blog post, we will discuss five ways to monitor your ML models in production.

What is ML monitoring?

Machine Learning (ML) monitoring is the continual oversight and evaluation of the performance of ML models over time. It is critical because ML model performance can worsen over time as data or the environment changes, a phenomenon known as "model drift." These issues can be identified through ML monitoring, which provides insights into the model's performance metrics, data quality, and overall application health.

Note: All the ML monitoring techniques discussed in this post can be implemented with the open-source ML monitoring library, whylogs, or the WhyLabs AI observability platform.

An example of ML monitoring within an AI application

ML monitoring for data drift

Data drift occurs when the input data to an ML model changes over time. The incoming data from production may no longer be similar to the data distribution used to train the model. As a result, the model's performance can degrade, leading to incorrect predictions.

One way to monitor data drift is to track the distribution of the input data and compare it to the data used to train the model. If the distributions differ significantly, it may be necessary to retrain the ML model.

Detecting data drift in the WhyLabs platform

Learn more about how to detect data drift with whylogs, our open-source data and ML monitoring library, or in WhyLabs.

Monitoring models for concept drift and performance

Concept drift can occur when the performance of an ML model decreases over time, even though there may not be significant data drift.

To monitor for concept drift, you can compare the model's predictions to actual outcomes, such as sales or customer satisfaction scores. If the model's predictions deviate from actual results, it may be necessary to retrain the model.

If you don’t have ground truth data for comparison you can try using performance estimation.

Monitoring ML performance metrics overtime

Learn how to monitor ML performance metrics in WhyLabs.

Monitoring ML pipelines for data quality

Bad data can occur from errors in data collection, sensor malfunction, or any number of pipeline bugs. Data quality can have a significant impact on the performance of ML models.

One way to monitor for bad data is to validate that the data is in the expected format and range using a set of defined parameters, such as the data should always be a numerical value above 0.

Created data quality validation tests with constraints in whylogs

Learn how to perform data quality validation for ML monitoring with whylogs.

Monitoring ML models for bias and fairness

Bias can occur when an ML model is trained on a dataset not representative of the population it is being used to predict.

To monitor for model bias in production data, you can examine how the model behaves on a specific segment or demographic.

Using WhyLabs to inspect model performance metrics for bias

Learn more about detecting bias and fairness with performance tracing in WhyLabs.

Monitor AI Explainability

AI explainability methods can help you understand why complex machine learning models are making predictions. One way to monitor the explainability of ML models is using libraries like SHAP to extract global feature importance of models.

These values can be logged and used in combination with the other metrics to obtain deep insights into model behavior.

Using ML explainability values to inspect input data by feature importance

Learn how to monitor global feature importance in WhyLabs.

Key takeaways for ML monitoring

Monitoring ML models in production is essential to ensure they continue to meet the expected results. By monitoring for data drift, model drift, data quality, bias, and explainability, businesses can identify issues and take action to maintain the accuracy and performance of their ML models. Implementing a robust monitoring system can help businesses to optimize their operations, reduce costs, and mitigate risks, ultimately leading to better outcomes for both businesses and their customers.

If you’re looking to get started with data and ML monitoring, we’re here to help! Here are 5 ways to take the next step in your model monitoring journey!

  1. Get started with whylogs - our open-source data logging and monitoring tool
  2. Start using the WhyLabs AI observatory for free
  3. Request a demo and consultation with a solutions engineer
  4. Join an upcoming live event for more hands-on experience
  5. Ask questions the Robust & Responsible AI Slack group

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