Version Tracking Data Model
MLflow's version tracking data model provides a structured approach to managing and analyzing different versions of your GenAI applications across their entire lifecycle. By organizing version metadata within MLflow's core entities, you can systematically track performance, debug regressions, and validate deployments across development, staging, and production environments.
Overview
Version tracking in MLflow integrates seamlessly with the core data model through strategic use of tags and metadata. This approach enables comprehensive version management while maintaining the flexibility to adapt to your specific deployment and development workflows.
Core Entities for Version Tracking
🧪 Experiment: The Version Container
An Experiment serves as the root container for all versions of your GenAI application. Within a single experiment, you can track multiple application versions, environments, and deployment states while maintaining a unified view of your application's evolution.
Key characteristics:
- Single namespace: One experiment contains all versions of your application
- Cross-version analysis: Compare performance across different versions within the same container
- Historical continuity: Maintain complete version history in one location
- Unified metadata: Consistent tagging and organization across all versions
📝 Traces: Version-Aware Execution Records
Each Trace represents a single execution of your application and carries version-specific metadata through tags. This enables granular tracking of how different versions perform in various contexts.
Version metadata captured in traces:
Standard vs Custom Version Tags:
| Tag Type | Purpose | Examples |
|---|---|---|
| Automatic | MLflow-populated metadata | mlflow.source.git.commit, mlflow.source.name |
| Standard | Reserved for specific meanings | mlflow.trace.session, mlflow.trace.user |
| Custom | Application-specific context | app_version, environment, deployment_id |
📊 Assessments: Version-Specific Quality Judgments
Assessments enable version-specific quality analysis by attaching evaluations to traces. This creates a foundation for comparing quality metrics across different versions and deployment contexts.
Assessment types for version tracking:
- Performance Feedback: Latency, throughput, resource usage
- Quality Feedback: Relevance, accuracy, helpfulness scores
- User Experience: Satisfaction ratings, usability metrics
- Regression Testing: Expected outputs for version validation
🎯 Scorers: Automated Version Analysis
Scorers provide automated evaluation functions that can detect version-specific performance patterns, regressions, and improvements. They transform raw trace data into actionable version insights.
📋 Evaluation Datasets: Version Testing Collections
Evaluation Datasets support systematic version testing by providing curated collections of inputs and expected outputs. These datasets enable consistent comparison across versions and deployment validation.
Dataset organization for version management:
- Regression Testing: Core functionality validation across versions
- Performance Benchmarking: Standardized performance measurement
- Feature Validation: New capability testing and verification
- Environment Testing: Deployment-specific scenario validation
🚀 Evaluation Runs: Version Comparison Engine
Evaluation Runs orchestrate systematic version comparisons by running different application versions against the same datasets and collecting scored results for analysis.
🏷️ Labeling Sessions: Human Version Review
Labeling Sessions organize traces from specific versions for human expert review, enabling qualitative assessment of version changes and edge case identification.
Version Tracking Workflow
The complete version tracking workflow integrates all data model entities to provide comprehensive version lifecycle management:
Advanced Version Management Patterns
Multi-Environment Version Progression
Track the same version as it progresses through different environments:
Feature Flag Version Analysis
Understand how feature flags impact different versions:
Version Rollback Tracking
Monitor the impact of version rollbacks:
Data Relationships and Dependencies
Understanding how version tracking entities relate to each other:
Key Benefits of the Version Tracking Data Model
🔍 Comprehensive Observability
- Cross-version visibility: Compare performance across all application versions
- Environment-specific insights: Understand how versions behave in different deployment contexts
- Historical analysis: Track application evolution over time
📊 Data-Driven Decision Making
- Regression detection: Automatically identify performance or quality regressions
- Improvement validation: Confirm that new versions deliver expected benefits
- Deployment confidence: Make informed decisions about production deployments
🔄 Efficient Development Workflow
- Systematic testing: Consistent evaluation processes across version changes
- Quick iteration: Rapid feedback on version performance and quality
- Risk mitigation: Early detection of issues before production deployment
🎯 Quality Assurance
- Automated evaluation: Consistent quality measurement across versions
- Human validation: Expert review processes for critical version changes
- Continuous monitoring: Ongoing assessment of production version performance
Integration with MLflow Ecosystem
The version tracking data model seamlessly integrates with MLflow's broader ecosystem:
Next Steps
To implement comprehensive version tracking using MLflow's data model:
- Track Versions & Environments: Learn to attach version metadata to traces
- Evaluation Workflows: Create systematic version comparison processes
- Query and Analysis: Master advanced querying for version analysis
- MLflow UI: Use the interface for version-specific trace exploration
MLflow's version tracking data model provides the conceptual foundation for systematic application lifecycle management, enabling confident deployments, quick regression detection, and data-driven version management decisions across your GenAI application's evolution.