MLflow Traces Data Model for GenAI
MLflow Traces capture the complete execution flow of your GenAI applications, providing detailed observability into how requests are processed from input to output. Built on OpenTelemetry standards, the trace data model offers rich context for debugging, performance analysis, and quality assessment of your GenAI systems.
Overview
A Trace represents a single execution of your GenAI application, containing all the steps, data transformations, and contextual information needed to understand what happened during that specific request.
Trace Structure: Two Core Components
📊 TraceInfo: Lightweight Metadata
TraceInfo provides essential metadata about the trace for quick navigation, filtering, and analysis without needing to examine detailed execution data.
📋 TraceData: Detailed Execution
TraceData contains the complete execution details, including all spans that represent individual operations within your GenAI application.
Span Architecture: The Building Blocks
Spans are the fundamental units that capture individual operations within your trace. Each span represents a specific step in your GenAI application's execution flow.
Specialized Span Types for GenAI
Different span types capture specific GenAI operations with tailored data structures:
Chat Model Spans
Chat model spans capture LLM interactions with special attributes for conversation data:
| Attribute | Purpose | Content |
|---|---|---|
| mlflow.chat.messages | Conversation history | List of system/user/assistant messages |
| mlflow.chat.tools | Available functions | Tool definitions for function calling |
Retriever Spans
Retriever spans capture document search operations with structured output:
| Field | Purpose | Content |
|---|---|---|
| page_content | Document text | Retrieved document content |
| metadata.doc_uri | Source location | Document source URI |
| metadata.chunk_id | Fragment identifier | Specific chunk reference |
| id | Unique identifier | Document chunk ID |
Tags: Searchable Context
Tags provide searchable metadata that enables powerful filtering and analysis capabilities:
Trace Lifecycle and Usage Patterns
Development to Production Flow
Relationship to Other MLflow Entities
Traces integrate with the broader MLflow ecosystem to enable comprehensive GenAI application management:
Data Analysis and Quality Patterns
Performance Analysis
Traces enable comprehensive performance monitoring through span timing and attributes:
- End-to-end latency: Total trace execution time
- Component bottlenecks: Individual span performance
- Resource utilization: Token usage, API calls, compute costs
- Error patterns: Failed spans and their characteristics
Quality Assessment
Trace data supports systematic quality evaluation:
- Input/output validation: Verify data transformations
- Conversation analysis: Chat model interaction patterns
- Retrieval effectiveness: Document search performance
- Error correlation: Link failures to specific conditions
Business Intelligence
Rich tagging enables business-focused analysis:
- User behavior: Session and user-based patterns
- Cost attribution: Resource usage by business unit
- Feature adoption: A/B testing and feature flag analysis
- Operational metrics: Environment and deployment performance
Benefits of the Trace Data Model
🔍 Complete Observability
- Hierarchical visibility: Understand nested operation relationships
- Rich context: Tags and attributes provide searchable metadata
- OpenTelemetry compatibility: Integrate with existing observability tools
📊 Data-Driven Decisions
- Performance optimization: Identify bottlenecks and improvement opportunities
- Quality tracking: Monitor application quality trends over time
- Cost management: Track resource usage and optimization opportunities
🔄 Development Integration
- Debug support: Detailed execution flow for troubleshooting
- Evaluation datasets: Create test cases from production traces
- Continuous improvement: Systematic quality enhancement workflows
🎯 Business Alignment
- User-centric analysis: Track experience by user segments
- Feature validation: Measure impact of new capabilities
- Compliance support: Audit trails and data governance
Getting Started with Trace Data
Understanding the trace data model enables several key workflows:
- 🔍 Trace Analysis: Query and filter traces for specific patterns
- 📊 Performance Monitoring: Track key metrics across your application
- 🧪 Quality Evaluation: Create systematic testing from trace data
- 💡 Continuous Improvement: Use insights to enhance your GenAI application
The hierarchical span structure, combined with rich metadata and specialized schemas, provides the foundation for comprehensive GenAI application observability and continuous improvement.