problem finding the result of mlflow experiment tracking in Azure Portal

[monajalal]

I converted this notebook to a Python script and run it natively inside an Azure Compute VM.

import mlflow
import mlflow.sklearn
import azureml.core
from azureml.core import Workspace
import matplotlib.pyplot as plt

# Check core SDK version number
print("SDK version:", azureml.core.VERSION)

ws = Workspace.from_config()

mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())

experiment_name = "LocalTrain-with-mlflow-sample"
mlflow.set_experiment(experiment_name)

import numpy as np
from sklearn.datasets import load_diabetes
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split

X, y = load_diabetes(return_X_y = True)
columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
data = {
    "train":{"X": X_train, "y": y_train},
    "test":{"X": X_test, "y": y_test}
}

print ("Data contains", len(data['train']['X']), "training samples and",len(data['test']['X']), "test samples")

# Create a run object in the experiment
model_save_path = "model"

with mlflow.start_run() as run:
    # Log the algorithm parameter alpha to the run
    mlflow.log_metric('alpha', 0.03)
    # Create, fit, and test the scikit-learn Ridge regression model
    regression_model = Ridge(alpha=0.03)
    regression_model.fit(data['train']['X'], data['train']['y'])
    preds = regression_model.predict(data['test']['X'])

    # Log mean squared error
    print('Mean Squared Error is', mean_squared_error(data['test']['y'], preds))
    mlflow.log_metric('mse', mean_squared_error(data['test']['y'], preds))

    # Save the model to the outputs directory for capture
    mlflow.sklearn.log_model(regression_model,model_save_path)

    # Plot actuals vs predictions and save the plot within the run
    fig = plt.figure(1)
    idx = np.argsort(data['test']['y'])
    plt.plot(data['test']['y'][idx],preds[idx])
    fig.savefig("actuals_vs_predictions.png")
    mlflow.log_artifact("actuals_vs_predictions.png")

ws.experiments[experiment_name]

Here's the result of running the notebook:

Where in Azure Portal I can access the results? Can you please share a link?