What Data Science & ML teams need to know about monitoring ML models in production

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.

Common Issues with Monitoring Machine Learning Models in Production

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:

• Performance degradation or model drift:
  The model’s performance decreases with time (or suddenly) due to different factors (discussed below) affecting its overall efficacy and functionality.
• Changes in requirements:
  Machine learning models should constantly adapt to changing business requirements which can invalidate trained ML models as they may not reflect the latest requirements. However, it is difficult to monitor how or what needs to change once a model is deployed in production.
• Lack of real data:
  The inadequate real-world data and ineffective data collection methodology can hinder the model monitoring process.
• Access to quality labeled data:
  Real-world data usually lacks data labels, making it difficult to evaluate the performance of deployed ML models.
• Model and data bias:
  The deployed ML models can have deep-rooted algorithmic or data biases (like extreme outliers or skewed labels), which are difficult to identify during training, resulting in unfavorable predictions in a production environment.
• Hidden feedback loops:
  Hidden feedback loops exist in models that rely heavily on dynamic behavior learning, where models are retrained and fine-tuned on the wrong features identified previously, producing highly biased systems.
• Underutilized data dependencies:
  Redundant or unnecessary features and libraries form underutilized data dependencies in ML models, resulting in a performance drop.
• Black box model evaluation:
  ML models are like a black box. They have hidden internal processing and behavioral details that are usually inaccessible or unknown, making it difficult for ML engineers to evaluate the system.
• Missing Data Lineage:
  A machine learning model should record the complete data flow from the data source to its destination. It is difficult to hold the data accountable for any performance degradation if this information is missing.

Now that we understand some of the model monitoring challenges – let’s look at some solutions.

How to Effectively Monitor Machine Learning Models in Production

ML models require practical strategies for monitoring their performance in a production environment. Some of the recommended approaches include:

• Health checks:
  Unhealthy data pipelines or model pipeline leakages can harm data quality. Frequent checks can help ensure the health of various resources like data and model pipelines, system utilization, memory usage, etc.
• Establish fair attributes:
  MLOps teams encourage establishing fair models for better decision-making. Understanding datasets and complete model behavior can guarantee fairness and eradicate biases.
• Building dashboard:
  The performance of machine learning models can be evaluated by building dashboard pages to visualize a special set of metrics depending on the objectives.
• Comparison of model’s predictions with real data:
  The ML model’s performance can be assessed by comparing its predictions with real-time data to observe the model’s actual performance in the production environment.
• Update system frequently:
  Tracking the system performance without frequent updating can sense when the quality drops. Updating models according to the requirements significantly improves product quality.
• Manual inspection for silent failure points identification:
  The machine learning models adjust to the un-updated features or table entries, gradually degrading the performance. Therefore, observing the statistics and manually inspecting such silent failure points can prevent model decay.
• Prepare descriptive documentation:
  Descriptive documentation is recommended to handle complex models with several features. Information about the modules enhances understandability and reduces the failure rate.
• Train and serve:
  To eliminate the gap between training and serving. For example, YouTube’s homepage logs features while serving and uses them for future training.
• MLOps:
  MLOps methodology aligns an effective machine learning model monitoring process with the training and deployment of models to ensure productivity.

Besides monitoring strategies, ML engineers can perform some tests to ensure that ML models are production-ready. Let’s discuss them below.

5 Machine Learning Model Monitoring Tests

The following tests can simplify the ML model monitoring process:

1. Input Data Transparency

   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.

2. Observe Dependencies

   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.

3. Analyzing the Computational Performance Metrics

   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.

4. Assessing the Model Age

   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.

5. Tracking the Numerical Training Values

   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.

Benefits of Effective Machine Learning Model Monitoring

ML model monitoring offers the following advantages:

• Enhance model performance: The model monitoring process timely highlights the weak links and encourages quick solutions.

• Boost model reliability: Machine Learning model monitoring ensures a trustworthy and well-maintained model.

• Better decision making: Well-descriptive dashboards and metrics evaluation assist teams in forming healthy strategies and making well-informed business decisions.

• Accelerate business growth: Teams that constantly monitor models in production to evaluate performance can generate greater revenue aligned with their business goals.

Best Practices for Machine Learning Model Monitoring in Production

Following are some of the best practices to appropriately monitor ML models in production:

• Balanced team collaboration: Team collaboration enables collective utilization of expertise where different members can monitor specialized tasks and metrics.

• Right tools selection: A flexible and easy-to-integrate tool with alerting mechanism should be selected to monitor models in production.

• Model testing: Test the models based on logs, charts, predictions, and metrics.

• Monitor drift: Track the changes in the deployed model predictions to prevent and resolve deterioration.

• Observe unreasonable results: Keep a record of outputs to trace any unexpected or faulty predictions.

• Set automated alerts and notifications: Automated alerts notify the teams when the metrics cross a set threshold enabling them to take immediate actions.

Build Production-Grade Machine Learning Models

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.

ML Observability with Aporia

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.

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