Why is it important to continuously monitor and iterate on a deployed machine learning model?
Understand the Problem
The question is asking about the significance of ongoing monitoring and improvement of a machine learning model after it has been deployed. The options discuss various aspects, including performance optimization, issue identification, security protection, and ensuring accuracy and relevance over time.
Answer
Continuously monitor and iterate to identify faults, maintain accuracy, and manage data drifts.
Continuously monitoring and iterating on a deployed machine learning model is crucial because it allows detection of faults, inconsistencies, and performance issues such as data drifts. It ensures the model remains accurate and effective over time, adapting to changing data patterns and environments.
Answer for screen readers
Continuously monitoring and iterating on a deployed machine learning model is crucial because it allows detection of faults, inconsistencies, and performance issues such as data drifts. It ensures the model remains accurate and effective over time, adapting to changing data patterns and environments.
More Information
Continuous monitoring helps in early detection of data and concept drifts, which if left unchecked, can lead to inaccurate predictions and decreased model performance. It also facilitates timely model updates and retraining.
Tips
A common mistake is neglecting the potential for data drift, leading to models that perform well initially but degrade over time.
Sources
- A Comprehensive Guide on How to Monitor Your Models in Production - neptune.ai
- Machine Learning Model Monitoring: Best Practices - Datadog - datadoghq.com
- Why is it Important to Constantly Monitor Machine Learning and ... - towardsdatascience.com
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