Tutorial Lengkap Azure Machine Learning: ML End-to-End di Azure

Azure Machine Learning adalah platform berbasis cloud untuk membangun, melatih, dan mendeploy model machine learning. Platform ini menyediakan environment MLOps komprehensif dengan tools untuk seluruh lifecycle ML.

Mengapa Azure Machine Learning?

• Platform terpadu: Manajemen lifecycle ML lengkap
• AutoML: Kemampuan machine learning otomatis
• Enterprise-ready: Security, compliance, dan governance
• Compute fleksibel: Dari notebooks hingga GPU clusters
• Integrasi: Konektivitas seamless dengan ekosistem Azure

• Workspaces
• Compute instances dan clusters
• Datastores dan datasets
• Experiments dan runs
• Models dan endpoints

Prerequisites

pip install azure-ai-ml azure-identity

Azure CLI
az login
az extension add -n ml

Quick Start

1. Buat Workspace

from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential
from azure.ai.ml.entities import Workspace

Autentikasi
credential = DefaultAzureCredential()

Buat workspace
workspace = Workspace(
    name="my-ml-workspace",
    location="eastus",
    displayname="ML Workspace",

    description="Azure ML workspace untuk proyek ML"
)

Buat ML client
mlclient = MLClient(
    credential=credential,
    subscriptionid="your-subscription-id",

    resourcegroupname="my-resource-group"

)

Buat workspace
mlclient.workspaces.begincreateorupdate(workspace).result()

print(f"Workspace dibuat: {workspace.name}")

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

mlclient = MLClient(
    credential=DefaultAzureCredential(),
    subscriptionid="your-subscription-id",

    resourcegroupname="my-resource-group",

    workspacename="my-ml-workspace"
)

print(f"Terhubung ke workspace: {mlclient.workspacename}")

Compute Resources

1. Buat Compute Instance

from azure.ai.ml.entities import ComputeInstance

computeinstance = ComputeInstance(

    name="my-compute-instance",
    size="StandardDS3v2",

    idletimebeforeshutdownminutes=60

)

mlclient.compute.begincreateorupdate(computeinstance).result()
print("Compute instance dibuat")

2. Buat Compute Cluster

from azure.ai.ml.entities import AmlCompute

computecluster = AmlCompute(

    name="cpu-cluster",
    type="amlcompute",
    size="StandardDS3v2",

    mininstances=0,
    maxinstances=4,

    idletimebeforescaledown=120

)

mlclient.compute.begincreateorupdate(computecluster).result()
print("Compute cluster dibuat")

3. GPU Cluster

gpucluster = AmlCompute(
    name="gpu-cluster",
    type="amlcompute",
    size="StandardNC6",
    mininstances=0,

    maxinstances=2,
    idletimebeforescaledown=300
)

mlclient.compute.begincreateorupdate(gpucluster).result()

from azure.ai.ml.entities import AzureBlobDatastore

datastore = AzureBlobDatastore(
    name="my-blob-datastore",
    accountname="mystorageaccount",
    containername="ml-data",

    credentials={
        "accountkey": "your-account-key"
    }
)

mlclient.datastores.createorupdate(datastore)

print("Datastore terdaftar")

from azure.ai.ml.entities import Data
from azure.ai.ml.constants import AssetTypes

File dataset
filedata = Data(
    name="training-data",