[FEATURE] Enhanced result export with MLOps tool integration

[najeed]

Is your feature request related to a problem? Please describe.
Current result export is limited. Enterprise users need integration with existing MLOps workflows, experiment tracking tools, and business intelligence systems for comprehensive evaluation management.

Describe the solution you'd like
Enhanced export and integration system supporting:

1. Advanced Export Formats

   • MLflow experiment logging
   • Weights & Biases integration
   • TensorBoard metrics export
   • Jupyter notebook report generation

2. Database Integration

   • PostgreSQL/MySQL result storage
   • Time-series databases for metrics
   • Vector databases for embedding analysis
   • Data warehouse integration

3. Business Intelligence

   • Tableau/PowerBI connector
   • Grafana dashboard templates
   • Custom API endpoints for BI tools
   • Automated report generation

Implementation Approach

# MLOps integrations
runner.export_mlflow(experiment_name="agent_eval")
runner.export_wandb(project="ai_agents", tags=["healthcare"])
runner.export_tensorboard(log_dir="./tensorboard_logs")

# Database export
runner.export_database(
    connection_string="postgresql://...",
    table_name="evaluation_results"
)

# BI tool integration
runner.generate_report(
    template="executive_summary",
    format="pdf",
    output="monthly_report.pdf"
)

Acceptance Criteria

• MLflow experiment tracking integration
• Weights & Biases logging support
• Database export capabilities
• BI tool connector templates
• Automated report generation
• API endpoints for external tool integration

Additional context
Critical for enterprise adoption where evaluation results need to integrate with existing data infrastructure and reporting workflows.

Estimated Effort

• Medium (1 week)