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Model monitoring is an essential stage of the MLOps pipeline that facilitates machine learning (ML) management practices. Implementing effective model monitoring enables ML engineers to detect underlying issues in the pipeline, mitigate problems and improve the deployed model.
Usually, ML models are built after rigorous training and testing. However, the model’s performance depreciates after deployment, which is critical for real-world time-sensitive operations. If left unchecked, the model will eventually lead to revenue loss, damage to brand reputation, poor customer experience, or other significant repercussions.
By the end of this article, you’ll understand the significance of ML model monitoring and various best practices to monitor machine learning models in production. Additionally, we’ll cover the common issues that disrupt the monitoring process and the strategies to avoid such challenges.
Monitoring ML models in production can be complicated due to operational and architectural limitations. However, understanding the common challenges faced after ML deployment can help resolve the issues. Some of the significant factors affecting the deployed ML models include:
Now that we understand some of the model monitoring challenges – let’s look at some solutions.
ML models require practical strategies for monitoring their performance in a production environment. Some of the recommended approaches include:
Besides monitoring strategies, ML engineers can perform some tests to ensure that ML models are production-ready. Let’s discuss them below.
The following tests can simplify the ML model monitoring process:
Input data and prediction results should both be transparent for analysis and comparison. Setting alerts can help notify when the values diverge from the set threshold.
The disconnection or upgrade of the source can cause issues in the machine learning models that collect data or resources from other systems. Therefore, working teams should subscribe to and read related notifications after studying the underlying dependencies.
The drop in standard performance metrics can be due to changes in training speed, serving latency, and memory consumption. Hence, computational scales are equally essential to track along with conventional performance metrics.
When retrained infrequently, machine learning models that have data dependence on other systems become outdated. Monitoring the age of the model helps determine the impact on prediction quality.
Features with invalid or incorrect numeric values affect model training without highlighting proper errors. Manually monitoring invalid or NaN occurrences can assist in a smooth training process.
ML model monitoring offers the following advantages:
Following are some of the best practices to appropriately monitor ML models in production:
Machine learning models undergo performance degradation right after deployment. Therefore, it is important to identify and examine the issues earlier to manage them effectively. Analyzing the summary of model statistics with unique metrics and logs helps in ML model monitoring.
Moreover, choosing an effective monitoring tool is critical to monitor models in production exhaustively. Businesses need to explore optimal solutions aligned with their desired business objectives.
Aporia’s full-stack, customizable ML observability solution gives data scientists and ML engineers the visibility, monitoring and automation, investigation tools, and explainability to understand why models predict what they do, how they perform in production over time, and where they can be improved.
Aporia provides customizable monitoring and observability for your machine learning models, enabling ML teams to fully trust their models and ensure that they are working as intended. With dynamic widgets and custom metrics, you can monitor prediction drifts, data drifts, missing values at input, freshness, F1 Score, etc. Try Aporia’s Free Community Edition to get a hands-on feeling or Book a Demo to see how Aporia’s ML observability can improve your models in production.