Azure AI Machine Learning – Overview

Azure AI Machine Learning (Azure ML) is a cloud‑based service that enables data scientists, developers, and IT professionals to build, train, and deploy machine‑learning models at scale.

Key capabilities

Automated ML – Quickly generate high‑quality models without extensive coding.
Designer (drag‑and‑drop) – Build pipelines visually.
ML Ops – Manage the complete machine‑learning lifecycle with CI/CD, monitoring, and governance.
Scalable compute – Use Azure Compute Instances, Clusters, and Kubernetes.
Open‑source integration – Support for PyTorch, TensorFlow, scikit‑learn, and more.

Sample code

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"
)

print(ml_client.workspaces.get("YOUR_WORKSPACE"))

Frequently asked questions

What is the difference between Azure ML Studio and Azure ML Designer?

Azure ML Studio is a web‑based interface for managing experiments, datasets, and models, while Azure ML Designer provides a visual, drag‑and‑drop environment for building pipelines without writing code.

How does Azure ML ensure model security?

Azure ML integrates with Azure Active Directory, role‑based access control (RBAC), and Azure Key Vault to protect data, models, and compute resources.