> For the complete documentation index, see [llms.txt](https://docs.rierino.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rierino.com/data-science/data-science-overview.md). # Data Science Overview Rierino provides low/no-code capabilities for customizing and managing data science models. ## **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).

Data Science App

## **Core Data Science capability areas** Data Science capabilities are organized into these areas: * **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. * **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. * **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. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.rierino.com/data-science/data-science-overview.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. 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.