Seldon Core is an open-source platform designed to deploy machine learning models on Kubernetes. It provides a scalable and flexible solution for managing and serving models in production environments. One of its key features is the ability to monitor model performance, including detecting data drift, which is crucial for maintaining model accuracy over time.
In this scenario, the symptom observed is that the data drift detection feature in Seldon Core is not functioning as expected. This can manifest as a lack of alerts or notifications when there is a significant change in the input data distribution, which could lead to degraded model performance.
The root cause of data drift detection not working is often due to incorrect configuration of the data drift detection parameters. Seldon Core relies on these configurations to monitor and compare the incoming data against a baseline to detect any significant deviations.
Key parameters include:
To resolve the issue of data drift detection not working, follow these steps:
For more detailed guidance, refer to the Seldon Core Documentation. Additionally, explore the Seldon Core GitHub Repository for community support and updates.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)