MR
Mayur Rathi
@mayurrathi
⭐ 40.7k GitHub stars

Azure Ai Ml Py

Azure Ai Ml Py is an data AI skill with a core value of |. It helps developers solve real-world problems in the data domain, boosting efficiency, automating repetitive tasks, and optimizing workflows.

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Last verified on: 2026-07-08

Quick Facts

Category data
Works With Claude
Source sickn33/antigravity-awesome-skills
Stars ⭐ 40.7k
Last Verified 2026-07-08
Risk Level Low
mkdir -p ./skills/azure-ai-ml-py && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/azure-ai-ml-py/SKILL.md -o ./skills/azure-ai-ml-py/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# 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}

🎯 Best For

  • Claude users
  • Data professionals
  • Analytics teams
  • Researchers

💡 Use Cases

  • Data pipeline auditing
  • Query optimization

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Azure Ai Ml Py to Your Work

    Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.

  4. 4

    Review and Refine

    Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.

❓ Frequently Asked Questions

How do I install Azure Ai Ml Py?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/azure-ai-ml-py/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Ignoring data quality

AI analysis inherits all data quality issues — profile your data first.

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