Model monitoring

Model monitoring, in the context of computing, programming, and cybersecurity, is a process of continually measuring, analyzing, and optimizing movements within a series of computer models. This process is used to enforce compliance with the rules and regulations these models’ actions are subject to, as well as to ensure the models are consistently producing accurate and reliable results. By monitoring the model, the performance of its operations can be tracked and any issues can be identified and remedied.

Model monitoring is composed of the following three steps:

1. Measurement: The first step in model monitoring is measuring the model’s performance in terms of the accuracy of its results and its adherence to defined rules and regulatory criteria. Measurement is accomplished by probing the model and recording its outputs.

2. Analysis: This step involves taking the results of the measurement and using them to determine if any issues exist which could cause the model to produce incorrect or unreliable results. Common sources of incorrect output include inaccurate or incomplete data, non-optimal model configuration, and incorrect parameter values.

3. Optimization: The last step of model monitoring is to identify any areas where the model’s performance can be improved, such as its accuracy or data processing speed. Optimization requires the use of sophisticated algorithms and requires expertise in the specific model being monitored.

Model monitoring is an essential tool for any organization relying on predictive analytics or AI-powered systems. It allows organizations to identify any issues with the model before they become major problems, and can help to ensure compliance with regulations. In addition, regular model monitoring can help ensure the model is consistently producing accurate results, which is critical for businesses relying on them for decisions.