# Data Science Monitoring

- [Dataflow with Kafka](https://docs.seldon.ai/seldon-core-2/user-guide/data-science-monitoring/dataflow.md): Explore how Seldon Core 2 uses data flow paradigm and Kafka-based streaming to improve ML model deployment with better scalability, fault tolerance, and data observability.
- [Model Performance Metrics](https://docs.seldon.ai/seldon-core-2/user-guide/data-science-monitoring/performance-tests.md): Learn how to run performance tests for Seldon Core 2 deployments, including load testing, benchmarking, and analyzing inference latency and throughput metrics.
- [Drift Detection](https://docs.seldon.ai/seldon-core-2/user-guide/data-science-monitoring/drift.md): Learn how to implement drift detection in Seldon Core 2 using Alibi-Detect integration for model monitoring and batch processing.
- [Outlier Detection](https://docs.seldon.ai/seldon-core-2/user-guide/data-science-monitoring/outlier.md): Learn how to implement outlier detection in Seldon Core using Alibi-Detect integration for model monitoring and anomaly detection.
- [Explainability](https://docs.seldon.ai/seldon-core-2/user-guide/data-science-monitoring/explainers.md): Learn how to implement model explainability in Seldon Core using Alibi-Explain integration for black box model explanations and pipeline insights.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.seldon.ai/seldon-core-2/user-guide/data-science-monitoring.md?ask=<question>
```

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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.