Anyone who works with machine learning models likely has an idea of how essential it is to have an effective ML model monitoring framework in place. This helps to ensure the model’s accuracy — and, in turn, its reliability — in making correct predictions.
But what makes ML model monitoring so important, and how do you build a proper model monitoring framework to track and evaluate an ML model in production?
Why is ML model monitoring important?
Model monitoring is essential throughout a model’s lifecycle to ensure it maintains accuracy over time. As the model processes new data, a certain amount of model degradation is inevitable. One of the biggest reasons behind this is the phenomenon of concept drift, described by one Cornell University study as being caused by “unforeseeable changes in the underlying distribution of streaming data over time.”
Model monitoring helps keep deployed models on track, offering the ability to monitor for things like model drift, negative feedback loops, and other indicators of an inaccurate-leaning model. With an ML model monitoring framework in place, it becomes much easier for Machine Learning Operations (MLOps) teams to:
- Catch and fix model inference violations right when they happen
- Detect outliers and quickly assess which ones are caused by specific model inputs
- Pinpoint data drift and contributing features to know when and how to retrain models
How do you evaluate ML models in production?
Effectively monitoring ML models in production entails two crucial elements: clear, actionable ML model monitoring metrics and high-quality machine learning model monitoring tools.
What are the most important ML model monitoring metrics?
There are a few different ML model monitoring metrics, each of which will help you understand how well the model is performing — and detect/triage issues before they become too problematic.
These metric types include:
- Classification metrics to evaluate the model’s classification abilities. These include accuracy, precision, recall, and more.
- Regression metrics to address predictive modeling problems based on regression. These include mean squared error, mean absolute error, ranking metrics, mean reciprocal rate, and more.
- Statistical metrics, which will vary depending on the type of data being analyzed. These metrics can include correlation, peak signal-to-noise, structural similarity index, and more.
- Natural language processing (NLP) metrics that evaluate how a model understands different languages, including translation accuracy. These primarily include perplexity and bilingual evaluation understudy scores.
- Deep learning metrics can provide valuable insights into the effectiveness and sophistication of a model’s neural networks. These include inception score and Frechet inception distance.
What are ML monitoring tools?
A wide variety of ML monitoring solutions are available, meaning organizations can seek out tools that are intuitive and aligned with the distinct needs of their MLOps lifecycle. When evaluating ML monitoring tools, some of the most important features to consider:
- An intuitive user interface that provides a shared view and greater understanding of in-production models’ performance
- Effective tools for catching — and fixing — model inaccuracies promptly
- Sophisticated detection capabilities to identify and triage data outliers
- An ability to pinpoint data drift, as well as potential contributing factors
- The ability to configure a machine learning model monitoring dashboard for real-time monitoring and issue detection
- Access to historical as well as current data, to better understand a model’s performance over time
- Easy integration with existing data environments and AI infrastructure
What are the essential components of an ML model monitoring framework?
An ML model monitoring framework provides a reliable system for monitoring and managing models both in training and production. It includes bringing the right tools, personnel, and model monitoring best practices together to provide a comprehensive and actionable view of model performance. Implementing a well-designed ML model monitoring framework helps organizations consistently:
- Detect shifts in data distribution or model/system performance
- Ensure effective practices around data integrity, accuracy, and segmentation
- Understand the contributing factors — and potential impact — of any performance variation or detected model bias
The best tools to support a monitoring framework will offer both model monitoring and explainable AI capabilities within a single AI observability platform, offering compelling benefits:
- The ability to lower costs by reducing the meantime for issue detection, identification, and resolution
- The ability to decrease the number of errors produced by the model, saving money as well as engineering time
- The ability to grow revenue through more efficient — and effective — model deployment and monitoring.
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