---
type: concept
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  pageDirectory: concepts
  sources:
    - databricks-feature-store-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - unified-ml-platform-on-databricks
    - UMPOD
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    - file: databricks-feature-store-databricks-on-aws.md
title: Unified ML Platform on Databricks
description: "Databricks provides a single platform for the entire ML workflow: data pipelines, feature engineering, model training, batch inference, model serving, and monitoring."
tags:
  - machine-learning
  - platform
  - databricks
  - mlops
timestamp: "2026-06-19T14:49:06.780Z"
---

---
title: Unified ML Platform on Databricks
summary: The entire ML workflow from data pipelines, feature engineering, model training, serving to monitoring takes place on a single Databricks platform.
sources:
  - databricks-feature-store-databricks-on-aws.md
kind: concept
createdAt: "2026-06-18T11:36:52.698Z"
updatedAt: "2026-06-18T11:36:52.698Z"
tags:
  - mlops
  - platform
  - databricks
aliases:
  - unified-ml-platform-on-databricks
  - UMPOD
confidence: 1
provenanceState: extracted
inferredParagraphs: 0
---

## Unified ML Platform on Databricks

Databricks provides a single platform for the entire machine learning workflow, including data pipelines that ingest raw data, create feature tables, train models, and perform batch inference. The platform also offers model and feature serving endpoints that are available with a single click and deliver millisecond-latency inference, along with data and model monitoring capabilities. ^[databricks-feature-store-databricks-on-aws.md]

### Key Component: Databricks Feature Store

The [Databricks Feature Store](/concepts/databricks-feature-store.md) acts as a central registry for the features used in AI and ML models. When feature tables and models are registered in [Unity Catalog](/concepts/unity-catalog.md), the platform provides built-in governance, lineage, point-in-time joins, and cross-workspace feature sharing and discovery. ^[databricks-feature-store-databricks-on-aws.md]

At inference time, models automatically look up the latest feature values. The Feature Store also supports on-demand computation of features for real-time applications, handling all feature computation tasks automatically. This design eliminates training/serving skew by ensuring that the feature computations used at inference are identical to those used during model training. It also simplifies client-side code, as all feature lookups and computation are managed by the Feature Store. ^[databricks-feature-store-databricks-on-aws.md]

### Benefits of the Unified Platform

- **Single platform for the entire model training workflow**: Data pipelines that ingest raw data, create feature tables, train models, and perform batch inference all run on the same Databricks environment. ^[databricks-feature-store-databricks-on-aws.md]
- **Elimination of training/serving skew**: Because feature computation and lookup are handled automatically by the Feature Store, the same feature logic used during training is consistently applied at inference time. ^[databricks-feature-store-databricks-on-aws.md]
- **Automatic lineage and governance**: Models trained with Feature Store features automatically track lineage back to those features. Unity Catalog governance features provide built-in oversight without manual effort. ^[databricks-feature-store-databricks-on-aws.md]

---

## Sources

- databricks-feature-store-databricks-on-aws.md

# Citations

1. [databricks-feature-store-databricks-on-aws.md](/references/databricks-feature-store-databricks-on-aws-b97fcf6e.md)
