Continuous monitoring of machine learning (ML) models is a crucial aspect of maintaining their performance and reliability in production. Once an ML model is deployed, it is essential to monitor its predictions over time to ensure that it continues to meet the desired performance metrics. This process helps identify issues such as model drift, where the model’s accuracy degrades due to changes in the underlying data distribution or other external factors.

Monitoring can involve tracking various metrics, including prediction accuracy, latency, and error rates. By continuously assessing these metrics, organizations can detect when a model’s performance begins to decline and take corrective actions, such as retraining the model with new data or adjusting its parameters. This proactive approach helps prevent erroneous predictions and minimizes the potential impact of model degradation on business operations ²⁴.

In summary, continuous monitoring is vital for sustaining the effectiveness of ML models, ensuring they adapt to evolving data and maintain their predictive power over time.