Azure Machine Learning SDK v2 for Python Client library for managing Azure ML resources: workspaces, jobs, models, data, and compute. Installation pip install azure-ai-ml Environment Variables AZURE_SUBSCRIPTION_ID = < your-subscription-id

AZURE_RESOURCE_GROUP

< your-resource-group

AZURE_ML_WORKSPACE_NAME

< your-workspace-name

Authentication from azure . ai . ml import MLClient from azure . identity import DefaultAzureCredential ml_client = MLClient ( credential = DefaultAzureCredential ( ) , subscription_id = os . environ [ "AZURE_SUBSCRIPTION_ID" ] , resource_group_name = os . environ [ "AZURE_RESOURCE_GROUP" ] , workspace_name = os . environ [ "AZURE_ML_WORKSPACE_NAME" ] ) From Config File from azure . ai . ml import MLClient from azure . identity import DefaultAzureCredential

Uses config.json in current directory or parent

ml_client

MLClient . from_config ( credential = DefaultAzureCredential ( ) ) Workspace Management Create Workspace from azure . ai . ml . entities import Workspace ws = Workspace ( name = "my-workspace" , location = "eastus" , display_name = "My Workspace" , description = "ML workspace for experiments" , tags = { "purpose" : "demo" } ) ml_client . workspaces . begin_create ( ws ) . result ( ) List Workspaces for ws in ml_client . workspaces . list ( ) : print ( f" { ws . name } : { ws . location } " ) Data Assets Register Data from azure . ai . ml . entities import Data from azure . ai . ml . constants import AssetTypes

Register a file

my_data

Data ( name = "my-dataset" , version = "1" , path = "azureml://datastores/workspaceblobstore/paths/data/train.csv" , type = AssetTypes . URI_FILE , description = "Training data" ) ml_client . data . create_or_update ( my_data ) Register Folder my_data = Data ( name = "my-folder-dataset" , version = "1" , path = "azureml://datastores/workspaceblobstore/paths/data/" , type = AssetTypes . URI_FOLDER ) ml_client . data . create_or_update ( my_data ) Model Registry Register Model from azure . ai . ml . entities import Model from azure . ai . ml . constants import AssetTypes model = Model ( name = "my-model" , version = "1" , path = "./model/" , type = AssetTypes . CUSTOM_MODEL , description = "My trained model" ) ml_client . models . create_or_update ( model ) List Models for model in ml_client . models . list ( name = "my-model" ) : print ( f" { model . name } v { model . version } " ) Compute Create Compute Cluster from azure . ai . ml . entities import AmlCompute cluster = AmlCompute ( name = "cpu-cluster" , type = "amlcompute" , size = "Standard_DS3_v2" , min_instances = 0 , max_instances = 4 , idle_time_before_scale_down = 120 ) ml_client . compute . begin_create_or_update ( cluster ) . result ( ) List Compute for compute in ml_client . compute . list ( ) : print ( f" { compute . name } : { compute . type } " ) Jobs Command Job from azure . ai . ml import command , Input job = command ( code = "./src" , command = "python train.py --data ${{inputs.data}} --lr ${{inputs.learning_rate}}" , inputs = { "data" : Input ( type = "uri_folder" , path = "azureml:my-dataset:1" ) , "learning_rate" : 0.01 } , environment = "AzureML-sklearn-1.0-ubuntu20.04-py38-cpu@latest" , compute = "cpu-cluster" , display_name = "training-job" ) returned_job = ml_client . jobs . create_or_update ( job ) print ( f"Job URL: { returned_job . studio_url } " ) Monitor Job ml_client . jobs . stream ( returned_job . name ) Pipelines from azure . ai . ml import dsl , Input , Output from azure . ai . ml . entities import Pipeline @dsl . pipeline ( compute = "cpu-cluster" , description = "Training pipeline" ) def training_pipeline ( data_input ) : prep_step = prep_component ( data = data_input ) train_step = train_component ( data = prep_step . outputs . output_data , learning_rate = 0.01 ) return { "model" : train_step . outputs . model } pipeline = training_pipeline ( data_input = Input ( type = "uri_folder" , path = "azureml:my-dataset:1" ) ) pipeline_job = ml_client . jobs . create_or_update ( pipeline ) Environments Create Custom Environment from azure . ai . ml . entities import Environment env = Environment ( name = "my-env" , version = "1" , image = "mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04" , conda_file = "./environment.yml" ) ml_client . environments . create_or_update ( env ) Datastores List Datastores for ds in ml_client . datastores . list ( ) : print ( f" { ds . name } : { ds . type } " ) Get Default Datastore default_ds = ml_client . datastores . get_default ( ) print ( f"Default: { default_ds . name } " ) MLClient Operations Property Operations workspaces create, get, list, delete jobs create_or_update, get, list, stream, cancel models create_or_update, get, list, archive data create_or_update, get, list compute begin_create_or_update, get, list, delete environments create_or_update, get, list datastores create_or_update, get, list, get_default components create_or_update, get, list Best Practices Use versioning for data, models, and environments Configure idle scale-down to reduce compute costs Use environments for reproducible training Stream job logs to monitor progress Register models after successful training jobs Use pipelines for multi-step workflows Tag resources for organization and cost tracking When to Use This skill is applicable to execute the workflow or actions described in the overview.