MLFlow LLM Evaluate

➺ Framework Components:

  • Model tracking
  • Experiment management
  • Metric logging
  • Deployment monitoring 

    • ➺ Implementation Details:

    Core Features 
  • Version control
  • Parameter tracking
  • Artifact storage
  • Results visualization

    • ➺ Evaluation Pipeline:

  • Data preparation
  • Model inference
  • Metric calculation
  • Result logging

    • ➺ Integration Points:

  • CI/CD systems
  • Monitoring tools
  • Alert systems
  • Dashboard creation 

    • ➺ Best Practices:

    python 

    # MLflow LLM evaluation example 

    import mlflow 

     

    with mlflow.start_run(): 

        # Log parameters 

        mlflow.log_params({ 

            “model_name”: “gpt-3.5”, 

            “temperature”: 0.7 

        }) 

         

        # Log metrics 

        mlflow.log_metrics({ 

            “accuracy”: accuracy_score, 

            “latency”: response_time 

        })