Azure AI Machine Learning Quickstarts

Python SDK Quickstart

Set up your environment, create a workspace, and run a basic training script using the Azure ML Python SDK.

# Install SDK
pip install azure-ai-ml

# Create a workspace
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

ml_client = MLClient(
    credential=DefaultAzureCredential(),
    subscription_id="YOUR_SUBSCRIPTION_ID",
    resource_group_name="YOUR_RESOURCE_GROUP",
    workspace_name="YOUR_WORKSPACE"
)

# Submit a training job
from azure.ai.ml.entities import Job
job = Job(
    code="./src",
    command="python train.py",
    environment="AzureML-sklearn-0.24-ubuntu20.04-py38-cpu:1",
    compute="cpu-cluster",
    experiment_name="quickstart-experiment"
)
ml_client.jobs.create_or_update(job)

CLI Quickstart

Use Azure CLI to create resources and run a simple training job without writing code.

# Login
az login

# Set defaults
az account set --subscription YOUR_SUBSCRIPTION_ID
az configure --defaults group=YOUR_RESOURCE_GROUP workspace=YOUR_WORKSPACE

# Create compute target
az ml compute create -n cpu-cluster --type amlcompute --size Standard_DS3_v2 --min-instances 0 --max-instances 4

# Submit a run
az ml job create -f job.yml

Azure Portal Quickstart

Launch the Azure Machine Learning studio and follow the guided steps to create a workspace and run a sample experiment.

Open Studio

Jupyter Notebook Quickstart

Start an interactive notebook in Azure ML and experiment with data, models, and pipelines.

%load_ext azureml.core
from azureml.core import Workspace, Experiment, Run

ws = Workspace.from_config()
experiment = Experiment(ws, "quickstart-notebook")
run = experiment.start_logging()
# Your notebook code here
run.complete()