pythonintermediate

MLflow Experiment Tracking in Python

Track ML experiments, log metrics, parameters, and artefacts with MLflow for reproducible training.

pythonPress ⌘/Ctrl + Shift + C to copy

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

mlflow.set_experiment('iris-classification')

with mlflow.start_run():
    params = {'n_estimators': 100, 'max_depth': 5, 'random_state': 42}
    mlflow.log_params(params)

    model = RandomForestClassifier(**params)
    model.fit(X_train, y_train)

    preds = model.predict(X_test)
    acc   = accuracy_score(y_test, preds)
    mlflow.log_metric('accuracy', acc)

    mlflow.sklearn.log_model(model, 'random_forest')
    print(f'Accuracy: {acc:.4f}')