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MLflow

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It tackles four primary functions: tracking experiments, packaging code into reproducible runs, sharing and deploying models, and providing a centralized model registry.

Core Components​

  • Tracking: Record and query experiments (code, data, config, results).
  • Projects: Package data science code in a reusable, reproducible form.
  • Models: Deploy machine learning models in diverse serving environments.
  • Registry: Centralized repository to collaboratively manage the full lifecycle of an MLflow Model.

Basic Usage​

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestRegressor

# Start an MLflow run
with mlflow.start_run():
    
    # Log parameters
    n_estimators = 100
    mlflow.log_param("n_estimators", n_estimators)
    
    # Train model
    model = RandomForestRegressor(n_estimators=n_estimators)
    # model.fit(X_train, y_train) 
    
    # Log metrics
    # mse = mean_squared_error(y_test, predictions)
    # mlflow.log_metric("mse", mse)
    
    # Log the model artifact
    # mlflow.sklearn.log_model(model, "random_forest_model")

Why it is essential for AI​

When you run 50 different experiments with different learning rates, datasets, and architectures, you will forget what produced the best result. MLflow keeps a rigorous, searchable database of every experiment you run.