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# Overview

> Introduction to Kumo Relational — the relational foundation model for predictive AI on relational data

[Kumo Relational](https://arxiv.org/abs/2604.12596) is a relational foundation model for predictive AI on structured, multi-table data. It generates predictions from relational context without requiring you to train a task-specific model first.

Kumo Relational learns from your data at query time. Describe a prediction task with **Predictive Query Language (PQL)**, a lightweight SQL-like interface, and receive results as a pandas DataFrame through the Python SDK.

You access Kumo Relational through **structured-data-models-client** (`pip install "structured-data-models-client[relational]"`, imported as `sdm_client`), NVIDIA's open-source Python client for structured-data foundation model NIMs. The same client serves both Kumo Relational (several related tables, joined as a graph) and Kumo Tabular (a single table) — the model you want is an argument to `predict()`, not a different package.

## What you can predict

#### [Classification and Regression](/sdgm/kumo-relational/user-guide/make-predictions/prediction-types)

Predict categories, Boolean outcomes, and numeric values.

#### [Forecasting](/sdgm/kumo-relational/user-guide/make-predictions/forecasting)

Predict numeric values over a future time horizon.

#### [Link Prediction](/sdgm/kumo-relational/user-guide/make-predictions/prediction-types)

Rank or recommend related entities.

## Get Started

#### [Install and Connect](/sdgm/kumo-relational/get-started/sdk-getting-started)

Install `structured-data-models-client[relational]` and connect to a NIM serving `kumo-relational`.

#### [Run Your First Prediction](/sdgm/kumo-relational/get-started/rfm)

Build a schema and run a prediction with `SDMClient`.

#### [Examples and Notebooks](/sdgm/kumo-relational/user-guide/examples-and-notebooks/notebooks)

Explore end-to-end predictive workflows and datasets.

#### [API Reference](/sdgm/kumo-relational/reference/python-api-reference)

Look up the Python SDK, PQL, and result schema.