Machine Learning (ML) Model Monitoring System

A Machine Learning (ML) Model Monitoring System is a software monitoring system that can support ML model monitoring tasks.

• AKA: ML Model Monitoring Platform, Machine Learning Model Performance Monitoring System.
• Context:
  • It can collect, analyze, and visualize key Performance Metrics such as accuracy, precision, recall, and F1 score to help stakeholders understand the model's performance.
  • It can detect and notify users of any significant changes in the model's performance, data quality, or data drift, enabling quick identification and resolution of issues.
  • It can provide historical tracking of model performance, allowing for trend analysis and identification of recurring issues.
  • It can integrate with existing data pipelines, infrastructure, and alerting systems to streamline monitoring.
  • It can facilitate collaboration between Data Scientists, ML Engineers, and other stakeholders for diagnosing and resolving issues with the model.
  • It can support automated model retraining and deployment to maintain model performance in dynamic environments.
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• Example(s):
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• Counter-Example(s):
  • ML Model Deployment System.
• See: ML Model Drift, Data Drift, Concept Drift, Model Retraining.

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References

2023

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  • A Machine Learning (ML) Model Monitoring System is a software tool or platform designed to track and manage the performance and Data Quality of Productionized ML Models in real-time or near-real-time.
  • Also known as: ML Model Monitoring Platform, Machine Learning Model Performance Monitoring System.
  • It can collect, analyze, and visualize key Performance Metrics such as accuracy, precision, recall, and F1 score to help stakeholders understand the model's performance.
  • It can detect and notify users of any significant changes in the model's performance, data quality, or data drift, enabling quick identification and resolution of issues.
  • It can provide historical tracking of model performance, allowing for trend analysis and identification of recurring issues.
  • It can integrate with existing data pipelines, infrastructure, and alerting systems to streamline the monitoring process.
  • It can facilitate collaboration between Data Scientists, ML Engineers, and other stakeholders for diagnosing and resolving issues with the model.
  • It can support automated model retraining and deployment to maintain model performance in dynamic environments.
  • Associated concepts: Data Drift, Concept Drift, Model Retraining, ML Engineer, Data Scientist, Productionized ML Models, Software Monitoring Task, Machine Learning (ML) Model Monitoring Task.

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