Tutorial Lengkap Azure MLflow Integration: Experiment Tracking dan Model Management

Azure Machine Learning menyediakan integrasi MLflow native untuk experiment tracking, model versioning, dan deployment. Tutorial ini mencakup penggunaan MLflow dengan Azure ML untuk manajemen lifecycle ML yang komprehensif.

Mengapa MLflow di Azure?

• Integrasi native: Konektivitas seamless Azure ML
• Open standard: Portable lintas platform
• Unified tracking: Experiments, models, artifacts
• Easy deployment: Deploy MLflow models langsung
• Collaboration: Berbagi experiments antar tim

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

Prerequisites

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

Azure CLI
az login

Setup

1. Koneksi ke Azure ML

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

Koneksi ke workspace
mlclient = MLClient(

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

)

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

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

Set tracking URI
mlflow.settrackinguri(trackinguri)

import os

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

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

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


Atau gunakan DefaultAzureCredential
from azure.identity import DefaultAzureCredential

credential = DefaultAzureCredential()

import mlflow

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

Atau buat dengan 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

Mulai 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 selesai")

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

with mlflow.startrun():

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

    predictions = model.predict(Xtest)

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

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

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