Databricks

About Databricks

Databricks is a data intelligence platform built on the lakehouse architecture, combining data engineering, analytics, and machine learning in one environment. For AI workloads, Mosaic AI Model Serving exposes custom models, fine-tuned models, and foundation models as managed endpoints, while MLflow handles experiment tracking, model versioning, and the registry through which models are promoted towards production. Many organisations treat Databricks as the system of record for both their data and the models trained on it.

What the integration does

Hyperpriors treats a Mosaic AI serving endpoint like any other model target. The harness routes agent requests to your endpoints with retries, timeouts, and fallback routes configured centrally rather than per service.

Evaluation suites run against the models behind those endpoints, so a new version registered in MLflow is measured against the same fixed suites as its predecessor before it takes traffic. Because serving infrastructure changes as well as model weights — runtime upgrades, endpoint configuration, dependency updates — Hyperpriors watches for behavioural drift across serving-stack upgrades, not only across model versions. Guardrails inspect inputs and outputs at runtime, and observability records every call with trace context, token usage, cost, and latency.

Promotion through the registry becomes a reviewed, measured change: the evaluation evidence sits alongside the model version it describes.

Get started

Connect your Databricks workspace and serving endpoints from the Hyperpriors dashboard, or contact us to talk through your deployment.