MLFlow for logging and retrieving data
MLflow is an open-source platform for managing the ML lifecycle. It can be used to keep track of experiments, packaging code into reproducible runs, and sharing and deploying models. In the following exercise, you will log some of the parameters of a training experiment for your heart disease model. mlflow is imported, and the trained heart disease model has been loaded for you.
Cet exercice fait partie du cours
End-to-End Machine Learning
Instructions
- Initialize an MLflow experiment named
"Logistic Regression Heart Disease Prediction". - Start a run, and log the trained models coefficient and intercept.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Initialize the MLflow experiment
____.____("Logistic Regression Heart Disease Prediction")
# Start a run, log model coefficients and intercept
with ____.____:
for idx, coef in enumerate(model.coef_[0]):
____.____(f"coef_{idx}", ____)
____.____("intercept", model.intercept_[0])
run_id = mlflow.active_run().info.run_id
print(run_id)