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MLflow Experiments Data Model for GenAI

MLflow Experiments serve as the top-level organizational container for all GenAI application development and production activities. An Experiment provides a unified namespace that brings together traces, models, datasets, evaluation runs, and other MLflow entities under a single cohesive framework for your GenAI application lifecycle.

Overview​

The Experiment acts as the central hub that connects all aspects of your GenAI application development, from initial prototyping through production deployment and ongoing optimization.

The Experiment as Organizational Foundation​

🎯 Single Application Focus​

Each Experiment represents one distinct GenAI application or service. Whether you're building a chatbot, document summarizer, or code assistant, all related work happens within a single Experiment container.

🔗 Unified Entity Management​

All MLflow entities associated with your GenAI application automatically inherit the Experiment context, creating natural relationships and enabling cross-entity analysis.

📊 Lifecycle Continuity​

From development through production, your Experiment maintains continuity across all phases of your application lifecycle.

GenAI Entities Within Experiments​

📝 Traces: Execution Records​

Traces capture individual runs of your GenAI application and are always associated with an Experiment.

Relationship to Experiment:

  • All traces belong to exactly one Experiment
  • Traces inherit Experiment-level context and settings
  • Cross-trace analysis happens within the Experiment scope

🤖 Models: AI System Definitions​

Models represent the AI systems and configurations used in your GenAI application.

Relationship to Experiment:

  • Models are registered within specific Experiments
  • Model versions track evolution of your GenAI application
  • Traces reference specific model versions for reproducibility

📋 Datasets: Evaluation Collections​

Datasets contain curated examples used for testing and evaluating your GenAI application.

Relationship to Experiment:

  • Datasets are scoped to specific Experiments
  • Enable consistent evaluation across model versions
  • Support systematic testing and validation workflows

🚀 Evaluation Runs: Systematic Testing​

Evaluation Runs orchestrate systematic testing of your GenAI application using datasets and scoring functions.

Relationship to Experiment:

  • Evaluation Runs belong to specific Experiments
  • Generate new Traces that become part of the Experiment
  • Enable systematic comparison across models and versions

📊 Assessments: Quality Judgments​

Assessments capture quality evaluations and performance judgments on Traces within your Experiment.

Relationship to Experiment:

  • Assessments are attached to Traces within the Experiment
  • Enable quality tracking across application versions
  • Support data-driven improvement decisions

🏷️ Labeling Sessions: Human Review​

Labeling Sessions organize human review workflows for Traces within your Experiment.

Relationship to Experiment:

  • Labeling Sessions operate on Traces within the Experiment
  • Generate Assessments that enrich the Experiment data
  • Enable expert validation of automated evaluations

Complete Experiment Ecosystem​

All GenAI entities work together within the Experiment to create a comprehensive development and production environment:

Benefits of Experiment-Centric Organization​

🎯 Unified Context​

  • All related entities share common metadata and settings
  • Cross-entity analysis happens naturally within the Experiment scope
  • Consistent organization across development and production

📊 Comprehensive Tracking​

  • Complete application lifecycle visibility in one location
  • Historical continuity from initial development through production
  • Version comparison and evolution tracking

🔄 Streamlined Workflows​

  • Natural integration between development, testing, and production
  • Automated relationship management between entities
  • Simplified navigation and discovery of related components

📈 Data-Driven Insights​

  • Holistic view of application performance and quality
  • Systematic comparison across models, versions, and deployments
  • Foundation for continuous improvement processes

Experiment Management Best Practices​

🏗️ Organizational Structure​

  • One Experiment per GenAI application: Maintain clear boundaries between different applications
  • Descriptive naming: Use clear, consistent naming conventions for Experiments
  • Metadata consistency: Apply consistent tagging and organization patterns

📊 Data Management​

  • Trace organization: Use consistent tagging for effective filtering and analysis
  • Dataset curation: Maintain high-quality evaluation datasets within each Experiment
  • Assessment strategy: Implement systematic quality measurement approaches

🔄 Workflow Integration​

  • CI/CD integration: Connect deployment pipelines to Experiment tracking
  • Automated evaluation: Set up systematic testing using Evaluation Runs
  • Continuous monitoring: Implement ongoing assessment of production performance

Getting Started with Experiments​

Setting up an Experiment for your GenAI application creates the foundation for comprehensive tracking and analysis:

  1. 🧪 Create Experiment: Establish the container for your GenAI application
  2. 📝 Enable Tracing: Capture execution data from your application runs
  3. 📋 Add Datasets: Create evaluation collections for systematic testing
  4. 🚀 Run Evaluations: Implement systematic quality and performance testing
  5. 📊 Analyze Results: Use the unified view to drive improvements

The Experiment provides the organizational backbone that makes all other MLflow GenAI capabilities possible, creating a structured approach to developing, testing, and maintaining high-quality GenAI applications.

Next Steps​

MLflow Experiments provide the essential organizational framework that unifies all aspects of GenAI application development, enabling systematic tracking, evaluation, and improvement of your AI systems.