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Workflow Overview

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Follow the data from preparation to prediction in three workflows: direct Python inference, task-specific training with Kumo Engine, and Relational inference through NIM. For Forecast and Anomaly, see the separate Kumo-TS workflow.

Prediction Workflows

The branches below are alternative execution paths. NIM is the deployed NVIDIA Inference Microservice that hosts the Relational model.

Scroll horizontally to view all three paths.

Where Execution Happens

Structured Data Models

Prepare typed tables, labeled context, and query rows, then pass them to a pretrained model running in your Python process. For relational inference, include the related tables the model needs. The selected model and workload determine device and memory requirements; check the installation guide.

Kumo Engine

Kumo Engine reads source tables and materializes a graph snapshot into a local index used for training and scoring. Source data can reside in a warehouse or object store; the graph index still needs local working storage on the compute running Kumo Engine.

A saved model bundle does not include source data, the graph index, or prediction outputs. To score in a later session, load the model and provide a compatible snapshot; retain the model and other artifacts separately.

Structured Data Models Client

The Structured Data Models Client tab documents Kumo Relational Client (kumo-relational-client). It builds a graph from connected tables and samples relevant records. It sends the prepared prediction context to a compatible Kumo Relational NIM endpoint, where the model runs, and returns the predictions to your application.

The client connects to an existing NIM deployment and does not require a GPU on the client host. The NIM deployment requires GPU compute. See Client and NIM setup for installation and connection instructions, and client platform requirements for supported environments.

Next Steps

Continue with the setup and task guides for your path: