Model Registry & Experiment Tracking with MLflow

1Concept

MLflow tracks machine learning experiments, logging hyperparameters (`mlflow.log_param`), metrics (`mlflow.log_metric`), and saving model artifacts (`mlflow.pytorch.log_model`) to a central Model Registry.

2Architecture Diagram

Training Run ---> [ MLflow Tracking Server ] ---> Records Params, Accuracy Curve & Model Artifacts

3Code Example

mlflow_sample = '''
import mlflow

mlflow.set_experiment("fraud_detection_v2")

with mlflow.start_run(run_name="xgboost_run_01"):
    mlflow.log_param("learning_rate", 0.05)
    mlflow.log_param("max_depth", 6)
    
    # Simulate training metric logging
    mlflow.log_metric("f1_score", 0.942)
    mlflow.log_metric("accuracy", 0.965)
    print("Experiment metrics logged to MLflow Registry successfully.")
'''
print("=== MLflow Experiment Tracking Architecture ===")
print(mlflow_sample.strip())

4Expected Output

=== MLflow Experiment Tracking Architecture ===
import mlflow

mlflow.set_experiment("fraud_detection_v2")

with mlflow.start_run(run_name="xgboost_run_01"):
    mlflow.log_param("learning_rate", 0.05)
    mlflow.log_param("max_depth", 6)
    
    # Simulate training metric logging
    mlflow.log_metric("f1_score", 0.942)
    mlflow.log_metric("accuracy", 0.965)
    print("Experiment metrics logged to MLflow Registry successfully.")

5Key Takeaways

• ✓MLflow Model Registry supports model staging tags (`Staging`, `Production`, `Archived`).
• ✓Logs training environment dependencies automatically for deterministic reproduction.
• ✓REST APIs allow serving registered models directly with `mlflow models serve`.