Complete Azure Machine Learning Tutorial: End-to-End ML on Azure

Azure Machine Learning is a cloud-based platform for building, training, and deploying machine learning models. It provides a comprehensive MLOps environment with tools for the entire ML lifecycle.

Why Azure Machine Learning?

• Unified platform: Complete ML lifecycle management
• AutoML: Automated machine learning capabilities
• Enterprise-ready: Security, compliance, and governance
• Flexible compute: From notebooks to GPU clusters
• Integration: Seamless Azure ecosystem connectivity

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

Prerequisites

pip install azure-ai-ml azure-identity

Azure CLI
az login
az extension add -n ml

Quick Start

1. Create Workspace

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

Authenticate
credential = DefaultAzureCredential()

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

    description="Azure ML workspace for ML projects"
)

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

    resourcegroupname="my-resource-group"

)

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

print(f"Workspace created: {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"Connected to workspace: {mlclient.workspacename}")

Compute Resources

1. Create 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 created")

2. Create 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 created")

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 registered")

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

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