Overview
Rierino provides low/no-code capabilities for customizing and managing data science models.
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 capabilities are organized into these areas:
ML Models: Configure traditional machine learning models, their inputs, and runtime settings in ML Models. Use this when you want consistent inference behavior across environments.
GenAI Models: Manage LLM providers, credentials, and agent-facing configuration in GenAI Models. This is the starting point for agent APIs and GenAI troubleshooting.
MCP Servers: Expose existing platform capabilities over MCP by configuring MCP Servers. 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. This is typically used for CEP and near-real-time enrichment pipelines.
Data Visualizations: Publish embedded dashboards and reporting views through Data Visualizations. Use this for observability of outcomes and business-facing reporting.
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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