Tutorial Azure DevOps untuk MLOps: CI/CD untuk Machine Learning

# Tutorial Lengkap Azure DevOps untuk MLOps: CI/CD untuk Machine Learning Azure DevOps menyediakan kemampuan CI/CD komprehensif untuk proyek machine learning. Tutorial ini mencakup pembangunan pipeli...

By Ruby Abdullah · · tutorial
AzureDevOpsMLOpsCI/CDAutomationPipeline

Tutorial Lengkap Azure DevOps untuk MLOps: CI/CD untuk Machine Learning

Azure DevOps menyediakan kemampuan CI/CD komprehensif untuk proyek machine learning. Tutorial ini mencakup pembangunan pipeline ML otomatis, deployment model, dan continuous delivery menggunakan Azure DevOps.

Mengapa Azure DevOps untuk MLOps?

Manfaat Utama:
  • Automasi end-to-end: Dari kode hingga deployment
  • Version control: Git repos untuk kode dan data
  • Orkestrasi pipeline: Workflow ML multi-stage
  • Integration: Integrasi native Azure ML
  • Collaboration: Pengembangan berbasis tim

Komponen:
  • Azure Repos: Git repositories
  • Azure Pipelines: Automasi CI/CD
  • Azure Artifacts: Package management
  • Azure Boards: Work tracking

Prerequisites

pip install azure-devops azure-ai-ml


Azure CLI


az login


az extension add --name azure-devops


az devops configure --defaults organization=https://dev.azure.com/myorg

Setup Proyek

1. Buat DevOps Project

# Buat project

az devops project create --name "MLOps-Project" --org https://dev.azure.com/myorg



Buat repository


az repos create --name "ml-models" --project "MLOps-Project"

2. Struktur Repository

ml-models/

├── src/


│   ├── train.py


│   ├── evaluate.py


│   └── score.py


├── tests/


│   └── testmodel.py


├── pipelines/


│   ├── train-pipeline.yml


│   ├── deploy-pipeline.yml


│   └── cd-pipeline.yml


├── infrastructure/


│   └── arm-templates/


├── environment.yml


├── requirements.txt


└── azure-pipelines.yml

3. Service Connection

# Buat service connection ke Azure

az devops service-endpoint azurerm create \


    --azure-rm-service-principal-id "your-sp-id" \


    --azure-rm-subscription-id "your-subscription-id" \


    --azure-rm-subscription-name "Your Subscription" \


    --azure-rm-tenant-id "your-tenant-id" \


    --name "azure-ml-connection"

CI Pipeline untuk ML

1. Basic CI Pipeline

# azure-pipelines.yml

trigger:


  branches:


    include:


  • main
    • 

  • develop
    • 

  paths:


    include:


  • src/
    • 

  • tests/
    • 



pool:


  vmImage: 'ubuntu-latest'



variables:


  pythonVersion: '3.9'



stages:


  • stage: Build
    • 

    displayName: 'Build dan Test'


    jobs:


  • job: BuildJob
    • 

        steps:


  • task: UsePythonVersion@0
    • 

            inputs:


              versionSpec: '$(pythonVersion)'


            displayName: 'Gunakan Python $(pythonVersion)'


  • script: |
    • 

              python -m pip install --upgrade pip


              pip install -r requirements.txt


              pip install pytest pytest-cov


            displayName: 'Install dependencies'


  • script: |
    • 

              python -m pytest tests/ --cov=src --cov-report=xml


            displayName: 'Jalankan tests'


  • task: PublishTestResults@2
    • 

            inputs:


              testResultsFiles: '*/test-.xml'


              testRunTitle: 'Python Tests'


  • task: PublishCodeCoverageResults@1
    • 

            inputs:


              codeCoverageTool: Cobertura


              summaryFileLocation: '$(System.DefaultWorkingDirectory)/*/coverage.xml'

2. Linting dan Code Quality

# Tambahkan ke azure-pipelines.yml

  • script: |
    • 

              pip install flake8 black mypy


              flake8 src/ --max-line-length=100


              black --check src/


              mypy src/


            displayName: 'Pemeriksaan kualitas kode'

Training Pipeline

1. ML Training Pipeline

# pipelines/train-pipeline.yml

trigger:


  branches:


    include:


  • main
    • 

  paths:


    include:


  • src/
    • 

  • data/
    • 



variables:


  • group: ml-variables
    • 

  • name: resourceGroup
    • 

    value: 'ml-rg'


  • name: workspaceName
    • 

    value: 'ml-workspace'


  • name: computeName
    • 

    value: 'cpu-cluster'



stages:


  • stage: Train
    • 

    displayName: 'Train Model'


    jobs:


  • job: TrainJob
    • 

        pool:


          vmImage: 'ubuntu-latest'


        steps:


  • task: UsePythonVersion@0
    • 

            inputs:


              versionSpec: '3.9'


  • task: AzureCLI@2
    • 

            displayName: 'Install Azure ML CLI'


            inputs:


              azureSubscription: 'azure-ml-connection'


              scriptType: 'bash'


              scriptLocation: 'inlineScript'


              inlineScript: |


                az extension add -n ml


  • task: AzureCLI@2
    • 

            displayName: 'Submit Training Job'


            inputs:


              azureSubscription: 'azure-ml-connection'


              scriptType: 'bash'


              scriptLocation: 'inlineScript'


              inlineScript: |


                az ml job create \


                  --file jobs/train-job.yml \


                  --resource-group $(resourceGroup) \


                  --workspace-name $(workspaceName) \


                  --set compute=$(computeName)

2. Definisi Training Job

# jobs/train-job.yml

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json


type: command


code: ./src


command: python train.py --data ${{inputs.data}} --output ${{outputs.model}}


inputs:


  data:


    type: urifolder


    path: azureml:training-data:1


outputs:


  model:


    type: urifolder


environment: azureml:sklearn-env:1


compute: azureml:cpu-cluster


experimentname: mlops-training


displayname: model-training-$(Build.BuildId)

3. Training Script

# src/train.py

import argparse


import os


import mlflow


import pandas as pd


from sklearn.ensemble import RandomForestClassifier


from sklearn.modelselection import traintestsplit


from sklearn.metrics import accuracyscore, f1score


import joblib



def main():


    parser = argparse.ArgumentParser()


    parser.addargument("--data", type=str, required=True)


    parser.addargument("--output", type=str, required=True)


    args = parser.parseargs()



    # Aktifkan autologging


    mlflow.autolog()



    # Load data


    df = pd.readcsv(os.path.join(args.data, "train.csv"))


    X = df.drop("target", axis=1)


    y = df["target"]



    Xtrain, Xtest, ytrain, ytest = traintestsplit(


        X, y, testsize=0.2, randomstate=42


    )



    # Train model


    with mlflow.startrun():


        model = RandomForestClassifier(nestimators=100, randomstate=42)


        model.fit(Xtrain, ytrain)



        # Evaluasi


        predictions = model.predict(Xtest)


        accuracy = accuracyscore(ytest, predictions)


        f1 = f1score(ytest, predictions, average="weighted")



        mlflow.logmetric("accuracy", accuracy)


        mlflow.logmetric("f1score", f1)



        # Simpan model


        os.makedirs(args.output, existok=True)


        joblib.dump(model, os.path.join(args.output, "model.joblib"))



        print(f"Accuracy: {accuracy:.4f}")


        print(f"F1 Score: {f1:.4f}")



if name == "main":


    main()

Pipeline Registrasi Model

1. Pipeline Register Model

# pipelines/register-pipeline.yml

stages:


  • stage: Register
    • 

    displayName: 'Register Model'


    dependsOn: Train


    jobs:


  • job: RegisterJob
    • 

        steps:


  • task: AzureCLI@2
    • 

            displayName: 'Register Model'


            inputs:


              azureSubscription: 'azure-ml-connection'


              scriptType: 'bash'


              scriptLocation: 'inlineScript'


              inlineScript: |


                # Dapatkan job terbaru


                JOBNAME=$(az ml job list \


                  --resource-group $(resourceGroup) \


                  --workspace-name $(workspaceName) \


                  --query "[0].name" -o tsv)



                # Register model


                az ml model create \


                  --name my-model \


                  --version $(Build.BuildId) \


                  --path azureml://jobs/$JOBNAME/outputs/model \


                  --resource-group $(resourceGroup) \


                  --workspace-name $(workspaceName)

2. Model dengan Metadata

# models/model.yml

$schema: https://azuremlschemas.azureedge.net/latest/model.schema.json


name: classification-model


path: azureml://jobs/{jobname}/outputs/model


description: Random Forest classifier ditraining pada data customer


properties:


  accuracy: 0.95


  framework: sklearn


  task: classification


tags:


  team: data-science


  environment: production

Pipeline Deployment

1. Deploy ke Online Endpoint

# pipelines/deploy-pipeline.yml

stages:


  • stage: DeployStaging
    • 

    displayName: 'Deploy ke Staging'


    jobs:


  • deployment: DeployStaging
    • 

        environment: staging


        strategy:


          runOnce:


            deploy:


              steps:


  • task: AzureCLI@2
    • 

                  displayName: 'Buat/Update Endpoint'


                  inputs:


                    azureSubscription: 'azure-ml-connection'


                    scriptType: 'bash'


                    scriptLocation: 'inlineScript'


                    inlineScript: |


                      # Buat endpoint jika belum ada


                      az ml online-endpoint create \


                        --name staging-endpoint \


                        --resource-group $(resourceGroup) \


                        --workspace-name $(workspaceName) \


                        --file endpoints/staging-endpoint.yml \


                        || true



                      # Buat/update deployment


                      az ml online-deployment create \


                        --name blue \


                        --endpoint-name staging-endpoint \


                        --resource-group $(resourceGroup) \


                        --workspace-name $(workspaceName) \


                        --file endpoints/staging-deployment.yml



                      # Set traffic


                      az ml online-endpoint update \


                        --name staging-endpoint \


                        --resource-group $(resourceGroup) \


                        --workspace-name $(workspaceName) \


                        --traffic "blue=100"


  • stage: TestStaging
    • 

    displayName: 'Test Staging'


    dependsOn: DeployStaging


    jobs:


  • job: SmokeTest
    • 

        steps:


  • task: AzureCLI@2
    • 

            displayName: 'Jalankan Smoke Tests'


            inputs:


              azureSubscription: 'azure-ml-connection'


              scriptType: 'bash'


              scriptLocation: 'inlineScript'


              inlineScript: |


                # Dapatkan endpoint key


                KEY=$(az ml online-endpoint get-credentials \


                  --name staging-endpoint \


                  --resource-group $(resourceGroup) \


                  --workspace-name $(workspaceName) \


                  --query "primaryKey" -o tsv)



                # Dapatkan endpoint URL


                URL=$(az ml online-endpoint show \


                  --name staging-endpoint \


                  --resource-group $(resourceGroup) \


                  --workspace-name $(workspaceName) \


                  --query "scoringuri" -o tsv)



                # Test endpoint


                curl -X POST "$URL" \


                  -H "Authorization: Bearer $KEY" \


                  -H "Content-Type: application/json" \


                  -d '{"features": [[5.1, 3.5, 1.4, 0.2]]}'


  • stage: DeployProduction
    • 

    displayName: 'Deploy ke Production'


    dependsOn: TestStaging


    condition: and(succeeded(), eq(variables['Build.SourceBranch'], 'refs/heads/main'))


    jobs:


  • deployment: DeployProduction
    • 

        environment: production


        strategy:


          runOnce:


            deploy:


              steps:


  • task: AzureCLI@2
    • 

                  displayName: 'Deploy ke Production'


                  inputs:


                    azureSubscription: 'azure-ml-connection'


                    scriptType: 'bash'


                    scriptLocation: 'inlineScript'


                    inlineScript: |


                      az ml online-deployment create \


                        --name blue-$(Build.BuildId) \


                        --endpoint-name production-endpoint \


                        --resource-group $(resourceGroup) \


                        --workspace-name $(workspaceName) \


                        --file endpoints/production-deployment.yml

2. Konfigurasi Endpoint

# endpoints/production-endpoint.yml

$schema: https://azuremlschemas.azureedge.net/latest/managedOnlineEndpoint.schema.json


name: production-endpoint


authmode: key



endpoints/production-deployment.yml


$schema: https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.schema.json


name: blue


endpointname: production-endpoint


model: azureml:classification-model:latest


instancetype: StandardDS3v2


instancecount: 2


codeconfiguration:


  code: ./scoring


  scoringscript: score.py


environment: azureml:sklearn-env:1

Blue-Green Deployment

1. Pipeline Blue-Green

# pipelines/blue-green-pipeline.yml

stages:


  • stage: DeployGreen
    • 

    displayName: 'Deploy Green'


    jobs:


  • job: DeployGreen
    • 

        steps:


  • task: AzureCLI@2
    • 

            displayName: 'Deploy Versi Green'


            inputs:


              azureSubscription: 'azure-ml-connection'


              scriptType: 'bash'


              scriptLocation: 'inlineScript'


              inlineScript: |


                # Deploy versi baru sebagai green


                az ml online-deployment create \


                  --name green \


                  --endpoint-name production-endpoint \


                  --resource-group $(resourceGroup) \


                  --workspace-name $(workspaceName) \


                  --file endpoints/green-deployment.yml


  • stage: TestGreen
    • 

    displayName: 'Test Green'


    dependsOn: DeployGreen


    jobs:


  • job: TestGreen
    • 

        steps:


  • task: AzureCLI@2
    • 

            displayName: 'Test Green Deployment'


            inputs:


              azureSubscription: 'azure-ml-connection'


              scriptType: 'bash'


              scriptLocation: 'inlineScript'


              inlineScript: |


                python tests/integrationtests.py --deployment green


  • stage: ShiftTraffic
    • 

    displayName: 'Shift Traffic'


    dependsOn: TestGreen


    jobs:


  • job: ShiftTraffic
    • 

        steps:


  • task: AzureCLI@2
    • 

            displayName: 'Traffic Shift Bertahap'


            inputs:


              azureSubscription: 'azure-ml-connection'


              scriptType: 'bash'


              scriptLocation: 'inlineScript'


              inlineScript: |


                # 10% ke green


                az ml online-endpoint update \


                  --name production-endpoint \


                  --traffic "blue=90 green=10"



                sleep 300  # Monitor 5 menit



                # 50% ke green


                az ml online-endpoint update \


                  --name production-endpoint \


                  --traffic "blue=50 green=50"



                sleep 300



                # 100% ke green


                az ml online-endpoint update \


                  --name production-endpoint \


                  --traffic "blue=0 green=100"


  • stage: Cleanup
    • 

    displayName: 'Cleanup Deployment Lama'


    dependsOn: ShiftTraffic


    jobs:


  • job: Cleanup
    • 

        steps:


  • task: AzureCLI@2
    • 

            displayName: 'Hapus Blue Deployment'


            inputs:


              azureSubscription: 'azure-ml-connection'


              scriptType: 'bash'


              scriptLocation: 'inlineScript'


              inlineScript: |


                az ml online-deployment delete \


                  --name blue \


                  --endpoint-name production-endpoint \


                  --yes

Pipeline Monitoring

1. Model Monitoring

# pipelines/monitor-pipeline.yml

schedules:


  • cron: "0 0   "  # Harian tengah malam
    • 

    displayName: Monitoring harian


    branches:


      include:


  • main
    • 



stages:


  • stage: Monitor
    • 

    displayName: 'Model Monitoring'


    jobs:


  • job: MonitorJob
    • 

        steps:


  • task: AzureCLI@2
    • 

            displayName: 'Cek Model Drift'


            inputs:


              azureSubscription: 'azure-ml-connection'


              scriptType: 'bash'


              scriptLocation: 'inlineScript'


              inlineScript: |


                python monitoring/checkdrift.py \


                  --endpoint production-endpoint \


                  --threshold 0.1


  • task: AzureCLI@2
    • 

            displayName: 'Cek Performance'


            inputs:


              azureSubscription: 'azure-ml-connection'


              scriptType: 'bash'


              scriptLocation: 'inlineScript'


              inlineScript: |


                python monitoring/checkperformance.py \


                  --endpoint production-endpoint \


                  --accuracy-threshold 0.85

2. Script Deteksi Drift

# monitoring/checkdrift.py

import argparse


from azure.ai.ml import MLClient


from azure.identity import DefaultAzureCredential



def checkdrift(endpointname, threshold):


    mlclient = MLClient.fromconfig(credential=DefaultAzureCredential())



    # Dapatkan prediksi terbaru


    # Bandingkan dengan distribusi training


    # Alert jika drift terdeteksi



    driftscore = calculatedrift()



    if driftscore > threshold:


        print(f"##vso[task.logissue type=warning]Data drift terdeteksi: {driftscore}")


        print("##vso[task.setvariable variable=driftDetected]true")


    else:


        print(f"Drift score: {driftscore} - dalam rentang yang diterima")


        print("##vso[task.setvariable variable=driftDetected]false")



if name == "main":


    parser = argparse.ArgumentParser()


    parser.addargument("--endpoint", required=True)


    parser.addargument("--threshold", type=float, default=0.1)


    args = parser.parseargs()


    checkdrift(args.endpoint, args.threshold)

Variable Groups

1. Buat Variable Group

# Buat variable group

az pipelines variable-group create \


  --name "ml-variables" \


  --variables \


    AZURESUBSCRIPTIONID="your-subscription-id" \


    AZURERESOURCEGROUP="ml-rg" \


    AZUREMLWORKSPACE="ml-workspace" \


  --authorize

2. Gunakan di Pipeline

variables:

  • group: ml-variables
    • 

  • name: environment
    • 

    value: 'production'



steps:


  • script: |
    • 

      echo "Deploying ke $(AZUREML_WORKSPACE)"

Best Practices

1. Pipeline Templates

# templates/train-template.yml

parameters:


  • name: computeTarget
    • 

    type: string


  • name: experimentName
    • 

    type: string



steps:


  • task: AzureCLI@2
    • 

    inputs:


      scriptType: 'bash'


      inlineScript: |


        az ml job create \


          --compute ${{ parameters.computeTarget }} \


          --experiment-name ${{ parameters.experimentName }}



Penggunaan di pipeline utama


stages:


  • stage: Train
    • 

    jobs:


  • template: templates/train-template.yml
    • 

        parameters:


          computeTarget: cpu-cluster


          experimentName: my-experiment

2. Environment Approvals

# Konfigurasi environment dengan approvals

environments:


  • name: production
    • 

    resourceName: production


    resourceType: VirtualMachine


    tags: production

Kesimpulan

Azure DevOps untuk MLOps menyediakan:

  • Automation: CI/CD end-to-end
  • Version control: Tracking kode dan model
  • Orkestrasi pipeline: Workflow multi-stage
  • Strategi deployment: Blue-green, canary
  • Monitoring: Monitoring berkelanjutan

    • Key takeaways:

    • Struktur repos untuk proyek ML
    • Gunakan pipeline multi-stage
    • Implementasikan testing yang proper
    • Gunakan blue-green deployments
    • Monitor model secara berkelanjutan

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