---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 0053b092bc6f42e9a1bc350afef261bb29be619745e939b15be248986ccf208a
  pageDirectory: concepts
  sources:
    - add-context-to-traces-databricks-on-aws.md
  confidence: 0.96
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - environment-and-version-tracking-in-mlflow-traces
    - Version Tracking in MLflow Traces and Environment
    - EAVTIMT
  citations:
    - file: add-context-to-traces-databricks-on-aws.md
title: Environment and Version Tracking in MLflow Traces
description: Tracking execution environment (dev/staging/prod) and application version metadata for debugging and performance analysis across deployments
tags:
  - mlflow
  - tracing
  - deployment
  - environments
timestamp: "2026-06-19T21:59:12.822Z"
---

# Environment and Version Tracking in MLflow Traces

**Environment and Version Tracking in MLflow Traces** enables you to capture execution context — such as deployment stage, application version, and environment type — alongside trace data for debugging, performance analysis, and regression detection across different releases.

## Overview

Tracking the execution environment and application version of your GenAI application allows you to debug performance and quality issues relative to the code. This metadata enables environment-specific analysis across development, staging, and production, performance and quality tracking and regression detection across app versions, and faster root cause analysis when issues occur. ^[add-context-to-traces-databricks-on-aws.md]

MLflow provides standardized metadata fields for common context types plus the flexibility to add custom metadata specific to your application. ^[add-context-to-traces-databricks-on-aws.md]

## Standard Metadata Fields

MLflow provides standard metadata fields for environment and version tracking. When you use these standardized fields, MLflow automatically enables filtering and grouping in the UI. ^[add-context-to-traces-databricks-on-aws.md]

| Metadata Field | Description | Example Values |
|---|---|---|
| `mlflow.source.type` | The execution environment type | `development`, `staging`, `production` |
| `mlflow.source.name` | The source name or identifier | `chatbot_v2`, `recommendation-engine` |

These fields are immutable once the trace is logged, making them ideal for permanent identifiers like deployment environment and application version. ^[add-context-to-traces-databricks-on-aws.md]

## Implementation

### Setting Environment and Version Metadata

To add environment and version context to traces, use [`mlflow.update_current_trace()`](https://mlflow.org/docs/latest/api_reference/python_api/mlflow.html?highlight=trace#mlflow.update_current_trace) during your application's execution. For deployment metadata such as environments and versions, your application should generally extract the metadata from environment variables rather than having the metadata hard-coded into the application. Environment variables simplify the deployment process: ^[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"),
        "mlflow.trace.user": user_id,
        "mlflow.trace.session": session_id,
    },
    tags={
        "query_category": "chat",
    },
)
```

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

After your application completes and a trace is logged, `tags` are mutable, but `metadata` are immutable in the logged trace. ^[add-context-to-traces-databricks-on-aws.md]

### Accessing Environment and Version Metadata

To access metadata and tags in trace logs, use the `metadata` and `tags` fields in the pandas DataFrame returned by `mlflow.search_traces()`, or use the `Trace.info.trace_metadata` and `Trace.info.tags` fields from `Trace` objects. ^[add-context-to-traces-databricks-on-aws.md]

## Automatically Populated Metadata

MLflow automatically sets certain standard metadata fields based on your execution environment. You can override any of the automatically populated metadata fields using `mlflow.update_current_trace()`. This is useful when the automatic detection does not meet your requirements. For example, override the execution environment value using: ^[add-context-to-traces-databricks-on-aws.md]

```python
mlflow.update_current_trace(metadata={"mlflow.source.name": "custom_name"})
```

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

## Custom Metadata for Application Context

You can use custom `metadata` keys to capture any other application-specific context. For example, you might want to attach information such as: ^[add-context-to-traces-databricks-on-aws.md]

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

## Complete Context Tracking

Production applications need to track multiple pieces of context simultaneously. Best practice is to combine environment, version, user, and session tracking together for complete traceability: ^[add-context-to-traces-databricks-on-aws.md]

```python
mlflow.update_current_trace(
    metadata={
        "mlflow.source.type": os.getenv("APP_ENVIRONMENT", "development"),
        "mlflow.trace.user": user_id,
        "mlflow.trace.session": session_id,
        "app_version": os.getenv("APP_VERSION", "1.0.0"),
        "deployment_region": os.getenv("DEPLOYMENT_REGION", "us-east-1"),
    },
    tags={
        "feature_flags": json.dumps(active_feature_flags),
    },
)
```

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

## Best Practices

1. **Populate metadata from environment variables** rather than hard-coding values to simplify the deployment process across different environments. ^[add-context-to-traces-databricks-on-aws.md]
2. **Combine context types** — track environment, version, user, and session 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. **Use consistent naming conventions** for custom metadata keys across your application.
5. **Set up dashboards** to monitor performance and quality across different environments and versions. ^[add-context-to-traces-databricks-on-aws.md]

## Requirements

MLflow 3 is required for context tracking. MLflow 2.x is not supported due to performance limitations and missing features essential for production use. ^[add-context-to-traces-databricks-on-aws.md]

For production deployments, install the `mlflow-tracing` package, which is optimized for production use with minimal dependencies and better performance characteristics. ^[add-context-to-traces-databricks-on-aws.md]

## Related Concepts

- [[MLflow Trace|MLflow Traces]] — The execution records that environment and version metadata enriches
- [MLflow Tracing](/concepts/mlflow-tracing.md) — The framework for capturing and analyzing traces
- [Add Context to Traces](/concepts/best-practices-for-adding-context-to-mlflow-traces.md) — General guide for adding different types of context to traces
- [Assessments on Traces](/concepts/assessments-on-traces.md) — Evaluation annotations that complement trace metadata

## 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)
