Complete Azure MLflow Integration Tutorial: Experiment Tracking and Model Management

Azure Machine Learning provides native MLflow integration for experiment tracking, model versioning, and deployment. This tutorial covers using MLflow with Azure ML for comprehensive ML lifecycle management.

Why MLflow on Azure?

• Native integration: Seamless Azure ML connectivity
• Open standard: Portable across platforms
• Unified tracking: Experiments, models, artifacts
• Easy deployment: Deploy MLflow models directly
• Collaboration: Share experiments across teams

• Tracking: Log experiments and metrics
• Projects: Package ML code
• Models: Model versioning and deployment
• Registry: Centralized model store

Prerequisites

pip install mlflow azureml-mlflow azure-ai-ml azure-identity

Azure CLI
az login

Setup

1. Connect to Azure ML

from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential
import mlflow

Connect to workspace
mlclient = MLClient(

    credential=DefaultAzureCredential(),
    subscriptionid="your-subscription-id",
    resourcegroupname="my-resource-group",
    workspacename="my-ml-workspace"

)

Get MLflow tracking URI
trackinguri = mlclient.workspaces.get().mlflowtrackinguri

print(f"Tracking URI: {trackinguri}")

Set tracking URI
mlflow.settrackinguri(trackinguri)

import os

Set Azure credentials for MLflow
os.environ["AZURETENANTID"] = "your-tenant-id"

os.environ["AZURECLIENTID"] = "your-client-id"

os.environ["AZURECLIENTSECRET"] = "your-client-secret"


Or use DefaultAzureCredential
from azure.identity import DefaultAzureCredential

credential = DefaultAzureCredential()

import mlflow

Set experiment
mlflow.setexperiment("my-ml-experiment")

Or create with tags
experiment = mlflow.createexperiment(

    name="classification-experiment",
    tags={
        "team": "data-science",
        "project": "customer-churn"
    }
)

import mlflow
from sklearn.ensemble import RandomForestClassifier
from sklearn.modelselection import traintestsplit
from sklearn.metrics import accuracyscore, f1score, precisionscore, recallscore

Start run
with mlflow.startrun(runname="random-forest-v1"):
    # Log parameters
    mlflow.logparam("nestimators", 100)
    mlflow.logparam("maxdepth", 10)
    mlflow.logparam("randomstate", 42)

    # Train model
    model = RandomForestClassifier(
        nestimators=100,

        maxdepth=10,
        randomstate=42

    )
    model.fit(Xtrain, ytrain)


    # Predictions
    predictions = model.predict(Xtest)

    # Log metrics
    mlflow.logmetric("accuracy", accuracyscore(ytest, predictions))

    mlflow.logmetric("f1score", f1score(ytest, predictions))

    mlflow.logmetric("precision", precisionscore(ytest, predictions))
    mlflow.logmetric("recall", recallscore(ytest, predictions))


    print("Run completed")

import matplotlib.pyplot as plt
from sklearn.metrics import confusionmatrix, ConfusionMatrixDisplay

with mlflow.startrun():

    # Train and predict
    model.fit(Xtrain, ytrain)

    predictions = model.predict(Xtest)

    # Create confusion matrix plot
    cm = confusionmatrix(ytest, predictions)
    disp = ConfusionMatrixDisplay(confusionmatrix=cm)

    disp.plot()
    plt.savefig("confusionmatrix.png")

    # Log artifact
    mlflow.logartifact("confusionmatrix.png")

    # Log directory of artifacts