---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 14c021960a6d475034163cb6bb67f8601f2c97b3c03613f608d7f7fa40b5084c
  pageDirectory: concepts
  sources:
    - add-context-to-traces-databricks-on-aws.md
  confidence: 0.95
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - mlflow-environment-and-version-tracking
    - Version Tracking and MLflow Environment
    - MEAVT
    - Environment Tracking
    - Environment tracking
  citations:
    - file: add-context-to-traces-databricks-on-aws.md
title: MLflow Environment and Version Tracking
description: Standardized metadata fields for tracking execution environment (development, staging, production) and application version in traces, enabling environment-specific analysis and regression detection.
tags:
  - mlflow
  - tracing
  - deployment
timestamp: "2026-06-19T08:52:14.971Z"
---

# MLflow Environment and Version Tracking

**MLflow Environment and Version Tracking** refers to the practice of capturing execution environment metadata and application version information in [[MLflow Trace|MLflow Traces]] to enable debugging, performance analysis, and regression detection across different deployments of GenAI applications.

## Overview

Production GenAI applications run across multiple environments — development, staging, and production — and evolve through multiple versions. MLflow provides standardized metadata fields to track this contextual information within traces, allowing teams to analyze how environment and version changes affect application behavior. ^[add-context-to-traces-databricks-on-aws.md]

## Standard Metadata Fields

MLflow defines several standard metadata fields for environment and version tracking:

| Field | Purpose |
|-------|---------|
| `mlflow.source.type` | Identifies the execution environment (e.g., `development`, `staging`, `production`) |
| `mlflow.source.name` | Identifies the application or source name |

These fields enable environment-specific analysis and faster root cause analysis when issues occur in production. ^[add-context-to-traces-databricks-on-aws.md]

## Automatically Populated Metadata

MLflow automatically sets certain standard metadata fields based on the execution environment. However, you can override any automatically populated metadata using `mlflow.update_current_trace()`. This is useful when the automatic detection does not meet your requirements — for example, overriding the execution environment value with a custom name. ^[add-context-to-traces-databricks-on-aws.md]

## Implementation

### Setting Environment and Version Metadata

For deployment metadata such as environments and versions, your application should generally extract the metadata from environment variables rather than hard-coding values. This simplifies the deployment process across different environments. ^[add-context-to-traces-databricks-on-aws.md]

```python
import mlflow
import os

# In your application logic
mlflow.update_current_trace(
    metadata={
        "mlflow.source.type": os.getenv("APP_ENVIRONMENT", "development"),
    }
)
```

### Adding Custom Metadata

You can use custom `metadata` keys to capture any other application-specific context, such as:

- Application version
- Deployment ID
- Deployment region
- Feature flags

^[add-context-to-traces-databricks-on-aws.md]

## Benefits

Environment and version tracking enables several important capabilities:

- **Environment-specific analysis** across development, staging, and production environments
- **Performance and quality tracking** and regression detection across application versions
- **Faster root cause analysis** when issues occur in specific environments or versions

^[add-context-to-traces-databricks-on-aws.md]

## Best Practices

1. **Use environment variables** — Populate metadata from environment variables rather than hard-coding values to simplify deployment across environments. ^[add-context-to-traces-databricks-on-aws.md]
2. **Combine context types** — Track user, session, and environment context together for complete traceability. ^[add-context-to-traces-databricks-on-aws.md]
3. **Override defaults thoughtfully** — Only override automatically populated metadata when necessary. ^[add-context-to-traces-databricks-on-aws.md]
4. **Regular analysis** — Set up dashboards to monitor version performance and environment-specific behavior. ^[add-context-to-traces-databricks-on-aws.md]

## Related Concepts

- [Add Context to Traces](/concepts/best-practices-for-adding-context-to-mlflow-traces.md) — General guidance on adding metadata and tags to traces
- [MLflow Tracing](/concepts/mlflow-tracing.md) — The tracing framework for GenAI applications
- User and Session Tracking — Tracking user identity and conversation sessions
- Trace Analysis — Querying and analyzing trace data
- [Production Monitoring for GenAI](/concepts/production-quality-monitoring-mlflow-genai.md) — Deploying monitoring with environment context

## Sources

- add-context-to-traces-databricks-on-aws.md

# Citations

1. [add-context-to-traces-databricks-on-aws.md](/references/add-context-to-traces-databricks-on-aws-1d18f107.md)
