azure-ai-ml-py

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# Azure Machine Learning SDK v2 for Python

Client library for managing Azure ML resources: workspaces, jobs, models, data, and compute.

## Scope and execution

Confirm subscription, tenant, workspace, region and budget before create/update or compute/job submission. Examples are fragments: training components, source files and environment definitions are not bundled. Select currently supported reviewed SDK/environment versions; historical image and `@latest` examples do not establish reproducibility. Use least-privilege identity and do not print private job data.

## Installation

```bash
pip install azure-ai-ml
```

## Environment Variables

```bash
AZURE_SUBSCRIPTION_ID=<your-subscription-id>
AZURE_RESOURCE_GROUP=<your-resource-group>
AZURE_ML_WORKSPACE_NAME=<your-workspace-name>
```

## Authentication

```python
import os
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

```python
import os
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

```python
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

```python
for ws in ml_client.workspaces.list():
    print(f"{ws.name}: {ws.location}")
```

## Data Assets

### Register Data

```python
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

```python
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

```python
from azure.ai.ml.entities import Model
from azure.ai.ml.constants import AssetTypes

model = Model(
    name="my-model",
    version="1",

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