Databricks Native Application

Introduction

Databricks users can deploy Kumo as an application running inside their native Databricks environments. This allows you to use Kumo’s advanced machine learning platform, which uses graph neural networks (GNNs) to deliver predictive analytics and insights directly from your relational data, while retaining complete control of your data inside Databricks.

With Kumo as a native Databricks application, your organization can realize the following benefits:

Build on Databricks

Kumo’s seamless integration with the Databricks data platform allows you to reuse existing Databricks workloads like ETL and EDA and code. By using your Unity Catalog (UC) and existing Databricks workflows, you can use the same fine-grained security mechanisms (for example, masking fields and limited group access), built-in data privacy and compliance controls, and collaboration tools, without data leaving your Databricks environment.

Improve Model Performance

Kumo improves accuracy by eliminating hand-crafted feature engineering, instead learning embeddings directly from raw relational data, while providing advanced controls that enable data scientists to add business value at each step. When used with the Databricks Photon query engine, you can access best-in-class compute and analytics runtime, and use Databricks Mosaic and Model Serving for unified building, deploying, and monitoring of LLMs.

Faster Time to Predictive Value

Kumo learns directly from your Databricks tables, eliminates time-consuming feature engineering, and uses AI to generate a model in hours, not months. Kumo’s automated pipelines keep models fresh so the AI always learns from the latest Databricks tables. With MLflow as a fully managed service in your Databricks workspace, you get model management and orchestration alongside your Kumo GNN-powered predictions.