How to Choose the Right Solution for Machine Learning Monitoring

The article discusses the importance of monitoring production machine learning models and provides insights into choosing the right solution for this purpose. It explains how monitoring helps identify issues like data drift, concept drift, bias, performance degradation, etc., before they impact businesses or customers. The challenges in model monitoring are also highlighted, such as changes in data, algorithms, and infrastructure. To adopt an ML model monitoring solution, one needs to consider factors like the type of data, algorithm, infrastructure, business metrics, domain trends, etc. Key features that a good monitoring solution should have include real-time monitoring, key alerts, model comparisons, dashboards, operational metrics, metadata store, collaboration, and explainability. The article concludes by emphasizing the importance of model monitoring in ensuring successful models and providing tips for effective monitoring.