Skip to contentazure-ai-ml-py · agentic-awesome-skills · FlowyAzure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.
One skill from agentic-awesome-skills.
$ npx -y skills add sickn33/agentic-awesome-skills --skill azure-ai-ml-py --agent claude-code
Installs just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/azure-ai-ml-py
👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.
📦 Ships with agentic-awesome-skills
</> SKILL.md
azure-ai-ml-py.SKILL.md
---
name: azure-ai-ml-py
description: Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.
risk: unknown
source: community
date_added: '2026-02-27'
---
# Azure Machine Learning SDK v2 for Python
Client library for managing Azure ML resources: workspaces, jobs, models, data, and compute.
## 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
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
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",
path="./model/",
type=AssetTypes.CUSTOM_MODEL,
description="My trained model"
)
ml_client.models.create_or_update(model)
```
### List Models
```python
for model in ml_client.models.list(name="my-model"):
print(f"{model.name} v{model.version}")
```
## Compute
### Create Compute Cluster
```python
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
```python
for compute in ml_client.compute.list():
print(f"{compute.name}: {compute.type}")
```
## Jobs
### Command Job
```python
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
```python
ml_client.jobs.stream(returned_job.name)
```
## Pipelines
```python
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
```python
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
```python
for ds in ml_client.datastores.list():
print(f"{ds.name}: {ds.type}")
```
### Get Default Datastore
```python
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
1. **Use versioning** for data, models, and environments
2. **Configure idle scale-down** to reduce compute costs
3. **Use environments** for reproducible training
4. **Stream job logs** to monitor progress
5. **Register models** after successful training jobs
6. **Use pipelines** for multi-step workflows
7. **Tag resources** for organization and cost tracking
## When to Use
This skill is applicable to execute the workflow or actions described in the overview.
## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
© 2026 Flowy · Free and open source
Built for Claude Code · Not affiliated with Anthropic
