Model Monitoring

TL;DR

Continuously track a model’s performance and accuracy after deployment.
Use metrics (e.g., precision, recall, accuracy) and bias-detection tools to detect problems.
Identify and correct deteriorations or biases to maintain model reliability.

Definition

Model monitoring is the process of continuously evaluating and assessing the performance and accuracy of a machine learning model over time.

Explanation

Model monitoring is an essential step in the development and deployment of machine learning systems. It enables ongoing assessment of how well a model predicts outcomes or classifies data and helps identify potential issues or biases that may emerge after training. Regular monitoring allows teams to detect performance deterioration or biased behavior and take corrective actions to preserve the model’s accuracy and reliability.

Examples

Performance metrics

Performance metrics such as precision, recall, and accuracy are calculated on a regular basis to assess the model’s ability to correctly predict outcomes or classify data. A decline in these metrics over time can indicate a problem that requires attention.

Bias detection tools

Bias detection tools are used to identify potential biases in a model’s predictions or classifications. For example, a model trained on a dataset heavily skewed toward a particular demographic or group may develop a bias toward that group; bias detection tools can reveal and help address this issue before it becomes problematic.

Use cases

Model monitoring is critical for machine learning systems deployed in fields such as healthcare, finance, self-driving cars, and natural language processing.

Notes or pitfalls

Deterioration in performance metrics over time can indicate underlying problems that need investigation and correction.
Detecting and addressing bias early prevents biased behavior from becoming a larger issue in production.

Performance metrics (precision, recall, accuracy)
Bias detection
Model deployment
Machine learning