---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 8300cb8a09c36f2a51cbeef1a8de2c841246ee4e1cfbe26660de81c900a6059d
  pageDirectory: concepts
  sources:
    - concepts-data-model-databricks-on-aws.md
  confidence: 0.95
  provenanceState: merged
  freshnessStatus: unverified
  aliases:
    - mlflow-application-versioning
    - MAV
    - application version
  citations:
    - file: concepts-data-model-databricks-on-aws.md
title: MLflow Application Versioning
description: Version management for GenAI apps through logged models (snapshots linking traces, prompts, and code) and prompts (version-controlled templates with variables and aliases like 'production').
tags:
  - mlflow
  - versioning
  - deployment
timestamp: "2026-06-19T09:21:19.942Z"
---

# MLflow Application Versioning

**MLflow Application Versioning** refers to the set of data model entities and practices in [MLflow](/concepts/mlflow.md) that track the evolution of a generative AI (GenAI) application over time. Versioning enables teams to compare app configurations, roll back changes, and link quality evaluations to specific snapshots of the application. In the MLflow data model for GenAI, application versioning is primarily handled by [Prompts](/concepts/prompt-versioning.md) and [Logged Models](/concepts/logged-models.md), both organized under an [MLflow experiments | experiment](/concepts/mlflow-experiment.md) container. ^[concepts-data-model-databricks-on-aws.md]

## Overview

An experiment in MLflow serves as a named container that groups all artifacts related to a single GenAI application, including all traces, evaluation runs, app versions, prompts, and quality assessments. While tracing is the only required part of the data model, versioning entities are highly recommended for systematic development and deployment management. ^[concepts-data-model-databricks-on-aws.md]

## Key Versioning Entities

### Prompts

Prompts are version-controlled templates for LLM prompts. Key characteristics:

- They maintain a **Git-like version history**, allowing developers to track changes over time. ^[concepts-data-model-databricks-on-aws.md]
- They support **{{variables}}** for dynamic generation of prompt content at inference time. ^[concepts-data-model-databricks-on-aws.md]
- They are **linked to evaluation runs**, enabling tracking of prompt quality across experiments. ^[concepts-data-model-databricks-on-aws.md]
- They support **aliases** (e.g., “production”) for deployment management, making it straightforward to promote a specific prompt version to a live environment. ^[concepts-data-model-databricks-on-aws.md]

### Logged Models

Logged models are snapshots of the entire application at a specific point in time. Key characteristics:

- They **link to the traces** generated by that application version, connecting observability data to the exact code and configuration that produced it. ^[concepts-data-model-databricks-on-aws.md]
- They **link to the prompts** used by that version, providing a full record of prompt templates. ^[concepts-data-model-databricks-on-aws.md]
- They **link to evaluation runs** that assess the quality of that version, allowing side-by-side comparisons. ^[concepts-data-model-databricks-on-aws.md]
- They track **application parameters** such as LLM temperature. ^[concepts-data-model-databricks-on-aws.md]
- They can act as a **metadata hub**, connecting a conceptual app version to its external code (e.g., a pointer to a Git commit). Optionally, a logged model can package the application’s code and configuration as a fully deployable artifact. ^[concepts-data-model-databricks-on-aws.md]

## How Versioning Supports Quality Improvement

Prompt and model versioning integrate tightly with evaluation workflows:

- [Evaluation Runs](/concepts/evaluation-runs.md) can be compared across different prompt or model versions to determine if a change improved or regressed quality. ^[concepts-data-model-databricks-on-aws.md]
- By aliasing a prompt as “production”, teams can monitor quality metrics in production and roll back to a previous version if issues arise. ^[concepts-data-model-databricks-on-aws.md]
- The experiment UI provides a visual interface to **manage versions and prompts**, making it easy to browse the history and select the right version for deployment. ^[concepts-data-model-databricks-on-aws.md]

## Related Concepts

- [MLflow Experiments](/concepts/mlflow-experiment.md)
- [Traces](/concepts/traces.md)
- [Evaluation Runs](/concepts/evaluation-runs.md)
- [Evaluation Datasets](/concepts/evaluation-datasets.md)
- [Assessments](/concepts/assessments.md)
- [MLflow GenAI Data Model](/concepts/mlflow-genai-data-model.md)
- [Prompt Registry](/concepts/prompt-registry.md)
- [Production Monitoring for GenAI](/concepts/production-quality-monitoring-mlflow-genai.md)

## Sources

- concepts-data-model-databricks-on-aws.md

# Citations

1. [concepts-data-model-databricks-on-aws.md](/references/concepts-data-model-databricks-on-aws-1534caf0.md)
