# Data Science Overview

## **What the Data Science app does**

The Data Science app is where you configure and govern ML and GenAI capabilities in a reusable way. You define model parameters, execution settings, and supporting assets so they can be invoked from real-time APIs (runners + sagas) or from batch jobs (Python processes, schedulers, and offline pipelines).

<figure><img src="/files/oaadtoHS2JIxmT7hOiWW" alt=""><figcaption><p>Data Science App</p></figcaption></figure>

## **Core Data Science capability areas**

Data Science capabilities are organized into these areas:

* **ML Models:** Configure traditional machine learning models, their inputs, and runtime settings in [ML Models](/data-science/ml-models.md). Use this when you want consistent inference behavior across environments.
* **GenAI Models:** Manage LLM providers, credentials, and agent-facing configuration in [GenAI Models](/data-science/genai-models.md). This is the starting point for agent APIs and GenAI troubleshooting.
* **MCP Servers:** Expose existing platform capabilities over MCP by configuring [MCP Servers](/data-science/mcp-servers.md). Use this when you want tools and microservices to be consumable by MCP clients.
* **Complex Event Processing:** Define real-time stream logic and windowed aggregations in [Complex Event Processing](/data-science/complex-event-processing.md). This is typically used for CEP and near-real-time enrichment pipelines.
* **Data Visualizations:** Publish embedded dashboards and reporting views through [Data Visualizations](/data-science/data-visualizations.md). Use this for observability of outcomes and business-facing reporting.

## **How Data Science components work together**

In practice, you configure assets here and invoke them elsewhere. A saga step might call an ML/GenAI handler for inference, a batch task might train or backfill features, and CEP flows might continuously compute derived signals. Keeping those definitions centralized makes changes traceable and easier to roll out safely.


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