Are ML Models Increasing Risks and Vulnerabilities for your Business, or Delivering Trusted Insights that Drive Transformational Decision-Making? 

There are a lot of Machine Learning (ML) monitoring tools on the market today, and it’s difficult to tell them apart, make decisions about which one is right for your needs, and then seamlessly implement those tools to start seeing the value. When considering ML Model Monitoring tools, here are a few critical areas to consider. 

1.      Drift – When looking at ML models in production, drift refers to feature, prediction, and performance drift. Drift alerts and drift detection identify issues such as – has something changed in the underlying assumptions that are causing the model to drift in the wrong direction? 

2.      Uncertainty Analysis & Outliers – Uncertainty Analysis in ML models is about confidence, or lack thereof, in the data and feature accuracy within the model. Uncertainty Analysis capabilities within ML monitoring tools validate data pre-deployment and re-evaluate data as part of a monitoring plan, over time. 

3.      Bias & Fairness – These types of metrics measure when models make predictions that are systematically distorted due to incorrect assumptions about data, inherently inaccurate data, inadvertently excluded data, or spurious relationships in data. Fairness tests can be applied to check for bias during data pre-processing, model training, and post-processing results of the model’s algorithm. 

4.      Adversarial Analysis – This monitoring method is also a training method that feeds models deceptive data to try and trick them. It both generates and detects deceptive input to models and tracks if a mistake is made in its predictions. 

5.      Data Quality & Integrity – Quality data is accurate and reliable, while data integrity ensures data is complete, valid, and consistent. To ensure trustworthy models, data quality rules and automated monitoring capabilities can help teams identify poor quality data used in training, pipelines, or during transformations, and also automate data cleansing and other tasks. 

6.      Robustness, Stability & Sensitivity – Models are robust and stable when they make accurate predictions that do not change significantly under varying conditions, and they have high sensitivity when they perform well in accurately detecting positive instances – in other words, their True Positive Rate is high.

7.      Modularity – Just as important as the depth of monitoring capabilities within individual tools and solutions is whether the tools you are evaluating have the ability to integrate seamlessly into your landscape, and into your MLOps workflows. Tools that are based on open-source technologies, have the ability to provide a unified user experience across multiple tools and technologies to deliver robust monitoring and explainability (or can tuck into your UI if that is your preference), and are built for complex, highly customized landscapes and business needs, is critical. 

It’s just as important to know what is in a monitoring solution when starting a search, as it is to know what’s not included. When evaluating monitoring solutions, be sure to look for these seven key aspects, to avoid surprises later, especially on missing capabilities. ML model monitoring is a critical aspect of running trusted, explainable models that bring fair and unbiased intelligence to empower decision-makers. Models that don’t create risk due to drift, bias, data quality problems, and other issues, but rather deliver the insights and trusted intelligence to transform and accelerate the business. 

Prevent risks and get the value with comprehensive, scalable ML model monitoring. Want to learn more? Contact us.