AI/ML Platform

Azure Machine Learning

Overview

Azure Machine Learning is Microsoft's enterprise-grade cloud-based machine learning platform that provides a comprehensive solution for data scientists and machine learning engineers to manage the entire machine learning model lifecycle. Through Azure Machine Learning Studio's web-based UI, Python SDK v2, and Azure CLI, it enables AutoML, custom model development, MLOps, and deployment all within a unified platform.

Details

Azure Machine Learning is a fully managed service built on Microsoft's cloud infrastructure that supports machine learning projects from development to production. As of 2025, it offers an improved API experience with Python SDK v2, complete MLflow integration, enhanced AutoML capabilities, and enterprise-grade security and governance features.

Key features include Azure Machine Learning Studio (a web-based integrated development environment), automated machine learning (AutoML) capabilities, MLOps pipelines, real-time and batch inference endpoints, and deep integration with the Azure ecosystem.

The platform supports diverse machine learning tasks including classification, regression, time series forecasting, natural language processing, and computer vision tasks. It also provides Jupyter Notebook environments, VS Code integration, and collaboration features for efficient team development.

Pros and Cons

Pros

• Comprehensive MLOps Solution: Provides consistent workflow from model development to deployment and monitoring
• AutoML Capabilities: Automatically generates high-quality models with minimal coding knowledge required
• Scalable Compute Resources: Automatic cluster scaling based on demand
• Azure Ecosystem Integration: Seamless integration with Azure Synapse Analytics, Azure DevOps, and Power BI
• Enterprise-Grade Security: Robust security through Azure Active Directory, RBAC, and VNet integration
• Full MLflow Support: Adherence to open-source standards and vendor lock-in avoidance
• No-Code Deployment: Easy endpoint deployment for MLflow models

Cons

• High Learning Curve: Initial complexity due to comprehensive feature set
• Complex Cost Management: Need to understand compute resource and storage billing structures
• Azure Dependency: Limited usage outside Microsoft ecosystem
• Regional Limitations: Some advanced features only available in specific Azure regions
• SDK v1 to v2 Migration: Migration work required for existing projects (SDK v1 support ends June 2026)

Reference Links

• Azure Machine Learning Official Page
• Azure Machine Learning Documentation
• Python SDK v2 Documentation
• Azure Machine Learning Studio
• MLflow and Azure ML Integration Guide
• AutoML Documentation

Code Examples

Basic Setup and Workspace Configuration

from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

# Connect to Azure Machine Learning workspace
credential = DefaultAzureCredential()
ml_client = MLClient(
    credential=credential,
    subscription_id="your-subscription-id",
    resource_group_name="your-resource-group",
    workspace_name="your-workspace-name"
)

# Get workspace details
workspace = ml_client.workspaces.get(name="your-workspace-name")
print(f"Workspace: {workspace.name}")
print(f"Location: {workspace.location}")
print(f"Resource Group: {workspace.resource_group}")

Data Management and Datasets

from azure.ai.ml.entities import Data
from azure.ai.ml.constants import AssetTypes

# Register data asset
data_asset = Data(
    name="customer-data",
    version="1",
    description="Customer analysis dataset",
    path="./data/customer_data.csv",
    type=AssetTypes.URI_FILE
)

# Create data asset
ml_client.data.create_or_update(data_asset)

# Get existing data asset
customer_data = ml_client.data.get(name="customer-data", version="1")
print(f"Data path: {customer_data.path}")

# Load data from datastore
import pandas as pd
df = pd.read_csv(customer_data.path)
print(f"Data shape: {df.shape}")
print(df.head())

AutoML and Custom Model Training

from azure.ai.ml import automl
from azure.ai.ml.entities import Model
from azure.ai.ml.constants import AssetTypes

# Configure AutoML classification task
classification_job = automl.classification(
    compute="cpu-cluster",
    experiment_name="customer-segmentation",
    training_data=customer_data,
    target_column_name="customer_segment",
    primary_metric="accuracy",
    n_cross_validations=5,
    enable_model_explainability=True
)

# Execute AutoML task
classification_job = ml_client.create_or_update(classification_job)
print(f"AutoML Job ID: {classification_job.name}")

# Custom model training job
from azure.ai.ml.entities import Command, Environment

# Define training script execution environment
environment = Environment(
    name="sklearn-env",
    description="Scikit-learn environment",
    conda_file="./environment.yml",
    image="mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04"
)

# Define training job
job = Command(
    code="./src",
    command="python train.py --data ${{inputs.training_data}} --output ${{outputs.model}}",
    environment=environment,
    compute="cpu-cluster",
    inputs={
        "training_data": customer_data
    },
    outputs={
        "model": "./outputs/model"
    }
)

# Execute training job
training_job = ml_client.create_or_update(job)

Model Deployment and Real-time Inference

from azure.ai.ml.entities import (
    ManagedOnlineEndpoint,
    ManagedOnlineDeployment,
    CodeConfiguration
)

# Create online endpoint
endpoint = ManagedOnlineEndpoint(
    name="customer-segmentation-endpoint",
    description="Customer segmentation API",
    auth_mode="key"
)

ml_client.online_endpoints.begin_create_or_update(endpoint).result()

# Model deployment
deployment = ManagedOnlineDeployment(
    name="blue",
    endpoint_name="customer-segmentation-endpoint",
    model=best_model,
    instance_type="Standard_F2s_v2",
    instance_count=1
)

ml_client.online_deployments.begin_create_or_update(deployment).result()

# Allocate traffic to endpoint
endpoint.traffic = {"blue": 100}
ml_client.online_endpoints.begin_create_or_update(endpoint).result()

# Test inference
import json
test_data = {
    "data": [
        [25, 50000, 2, 1],  # Age, Income, Purchase Count, Premium Member
        [45, 75000, 5, 0]
    ]
}

response = ml_client.online_endpoints.invoke(
    endpoint_name="customer-segmentation-endpoint",
    request_file=json.dumps(test_data)
)
print(f"Prediction results: {response}")

MLOps with Azure DevOps Integration

from azure.ai.ml.entities import Pipeline, PipelineJob
from azure.ai.ml.dsl import pipeline

@pipeline(
    description="Customer segmentation MLOps pipeline",
    default_compute="cpu-cluster"
)
def customer_segmentation_pipeline(pipeline_input_data):
    # Data preprocessing step
    data_prep_step = data_prep_component(
        input_data=pipeline_input_data
    )
    
    # Model training step
    train_step = train_component(
        training_data=data_prep_step.outputs.processed_data
    )
    
    # Model evaluation step
    evaluate_step = evaluate_component(
        model=train_step.outputs.model,
        test_data=data_prep_step.outputs.test_data
    )
    
    # Model registration step (if evaluation criteria are met)
    register_step = register_component(
        model=train_step.outputs.model,
        evaluation_results=evaluate_step.outputs.metrics
    )
    
    return {
        "trained_model": register_step.outputs.registered_model
    }

# Create and execute pipeline
pipeline_job = customer_segmentation_pipeline(
    pipeline_input_data=customer_data
)

pipeline_job = ml_client.jobs.create_or_update(
    pipeline_job,
    experiment_name="customer-segmentation-mlops"
)

# Azure DevOps integration (YAML configuration example)
"""
# azure-pipelines.yml
trigger:
- main

pool:
  vmImage: 'ubuntu-latest'

steps:
- task: AzureCLI@2
  inputs:
    azureSubscription: 'Azure ML Service Connection'
    scriptType: 'bash'
    scriptLocation: 'inlineScript'
    inlineScript: |
      az ml job create --file pipeline.yml --resource-group $(resourceGroup) --workspace-name $(workspaceName)
"""

Advanced Features and Azure AI Services Integration

# MLflow tracking integration
import mlflow
from azure.ai.ml import MLClient

# Configure MLflow tracking
mlflow.set_tracking_uri(ml_client.workspaces.get().mlflow_tracking_uri)
mlflow.set_experiment("customer-analysis")

with mlflow.start_run() as run:
    # Model training
    model = train_model(X_train, y_train)
    
    # Log metrics
    mlflow.log_metric("accuracy", accuracy_score(y_test, predictions))
    mlflow.log_metric("f1_score", f1_score(y_test, predictions, average='weighted'))
    
    # Log parameters
    mlflow.log_param("algorithm", "RandomForest")
    mlflow.log_param("n_estimators", 100)
    
    # Register model
    mlflow.sklearn.log_model(
        model, 
        "model",
        registered_model_name="customer-segmentation-model"
    )

# Azure Cognitive Services integration
from azure.cognitiveservices.language.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential

# Text analytics preprocessing
text_analytics_client = TextAnalyticsClient(
    endpoint="https://your-text-analytics.cognitiveservices.azure.com/",
    credential=AzureKeyCredential("your-key")
)

def preprocess_customer_feedback(feedback_texts):
    # Sentiment analysis
    sentiment_results = text_analytics_client.analyze_sentiment(
        documents=feedback_texts
    )
    
    # Key phrase extraction
    key_phrases = text_analytics_client.extract_key_phrases(
        documents=feedback_texts
    )
    
    # Integrate as features for machine learning model
    processed_features = []
    for sentiment, phrases in zip(sentiment_results, key_phrases):
        features = {
            'sentiment_score': sentiment.confidence_scores.positive,
            'negative_score': sentiment.confidence_scores.negative,
            'key_phrase_count': len(phrases.key_phrases)
        }
        processed_features.append(features)
    
    return processed_features

# Leverage batch inference endpoints
from azure.ai.ml.entities import BatchEndpoint, BatchDeployment

# Create batch endpoint
batch_endpoint = BatchEndpoint(
    name="customer-batch-scoring",
    description="Batch processing for large customer datasets"
)

ml_client.batch_endpoints.begin_create_or_update(batch_endpoint).result()

# Execute batch inference job
batch_job = ml_client.batch_endpoints.invoke(
    endpoint_name="customer-batch-scoring",
    input=large_customer_dataset,
    deployment_name="production"
)

# Continuous model monitoring
from azure.ai.ml.entities import DataDriftDetector

drift_detector = DataDriftDetector(
    name="customer-data-drift",
    baseline_data=training_data,
    target_data=production_data,
    compute="cpu-cluster",
    frequency="Day",
    alert_email="[email protected]"
)

ml_client.data_drift_detectors.create_or_update(drift_detector)