---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: c41aa6571d4abd031d80fd02514a009f6b2b22d6a3b041a42dfa3af854293408
  pageDirectory: concepts
  sources:
    - model-deployment-patterns-databricks-on-aws.md
  confidence: 0.95
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - deploy-models-pattern
    - DMP
    - Deploy Code Pattern
    - Deploy Models Approach
    - Model deployment patterns
  citations:
    - file: model-deployment-patterns-databricks-on-aws.md
title: Deploy Models Pattern
description: A model deployment pattern where the model artifact is trained in development, then tested in staging and promoted to production without retraining, separating model artifact lifecycle from code lifecycle.
tags:
  - mlops
  - deployment
  - databricks
timestamp: "2026-06-19T19:42:04.393Z"
---

# Deploy Models Pattern

The **Deploy Models Pattern** is a model deployment strategy where the model artifact is generated by training code in the development environment, then tested in staging, and finally deployed into production. This pattern contrasts with the [Deploy Code Pattern](/concepts/deploy-code-pattern-recommended.md) (recommended), where the training code itself is promoted through environments and the model is retrained in each environment. ^[model-deployment-patterns-databricks-on-aws.md]

In the deploy models pattern, the code lifecycle differs across execution environments. Final unit and integration testing of the model artifact occurs in the development environment, and the artifact is then promoted to staging and production without retraining. ^[model-deployment-patterns-databricks-on-aws.md]

## When to Use

Consider the deploy models pattern when one or more of the following apply:

- **Model training is very expensive or hard to reproduce.** Training the model only once in the development environment avoids repeated costs. ^[model-deployment-patterns-databricks-on-aws.md]
- **All work is done in a single Databricks workspace.** Without external repos or a CI/CD process, this pattern simplifies the handoff. ^[model-deployment-patterns-databricks-on-aws.md]
- **You are not working with external repos or a CI/CD process.** The pattern reduces the operational overhead of managing separate code repositories for each environment. ^[model-deployment-patterns-databricks-on-aws.md]

## Advantages

- **Simpler handoff for data scientists.** Data scientists can produce a model artifact and hand it off without needing to manage deployment of training code. ^[model-deployment-patterns-databricks-on-aws.md]
- **Single training pass.** When model training is expensive, the model is trained only once (in the development environment), saving compute resources. ^[model-deployment-patterns-databricks-on-aws.md]

## Disadvantages

- **Limited access to production data.** If production data is not accessible from the development environment (often true for security reasons), this architecture may not be viable. ^[model-deployment-patterns-databricks-on-aws.md]
- **Automated retraining is tricky.** You could automate retraining in the development environment, but the team responsible for deploying the model in production might not accept the resulting model as production-ready. ^[model-deployment-patterns-databricks-on-aws.md]
- **Separate deployment of supporting code.** Pipelines used for feature engineering, inference, and monitoring must be deployed to production independently of the model artifact. ^[model-deployment-patterns-databricks-on-aws.md]

## Environment and Lifecycle

Typically an environment (development, staging, or production) corresponds to a catalog in [Unity Catalog](/concepts/unity-catalog.md). For details on how to implement this pattern in Unity Catalog, see the [upgrade guide](https://docs.databricks.com/aws/en/machine-learning/manage-model-lifecycle/#promote). ^[model-deployment-patterns-databricks-on-aws.md]

In the deploy models pattern, the final environment for unit and integration testing is the **development environment**. In contrast, in the deploy code pattern, final unit and integration tests occur in the **staging environment**. ^[model-deployment-patterns-databricks-on-aws.md]

## Related Concepts

- [Deploy Code Pattern](/concepts/deploy-code-pattern-recommended.md)
- MLOps workflow
- CI/CD
- [Unity Catalog](/concepts/unity-catalog.md)
- Model Lifecycle Management

## Sources

- model-deployment-patterns-databricks-on-aws.md

# Citations

1. [model-deployment-patterns-databricks-on-aws.md](/references/model-deployment-patterns-databricks-on-aws-231ed92b.md)
