Getting Started with Azure Machine Learning Studio

Introduction

Azure Machine Learning Studio is a powerful cloud-based platform designed to streamline the entire machine learning (ML) lifecycle, from data preparation to model deployment. It provides an interactive workspace that allows users to develop, train, and deploy ML models efficiently.

Whether you are a data scientist, ML engineer, or business analyst, Azure ML Studio offers a robust ecosystem to accelerate AI development. It provides.

• Drag-and-drop capabilities
• Python SDK support
• Seamless integration with Azure cloud services

Azure Machine Learning is more than just a tool—it is a full-fledged AI-powered ecosystem that enables.

• Experimentation
• Automation
• Scalability

It also integrates with services like Azure Databricks, Azure Synapse Analytics, and Azure Cognitive Services.

In this article, we will explore how to,

• Set up an Azure ML workspace
• Ingest data into Azure ML Studio
• Run a basic experiment
• Train and evaluate a simple model

Step 1. Creating an Azure Machine Learning Workspace.

Prerequisites

Before starting, ensure that you have,

• An Azure account with an active subscription
• Access to the Azure Portal
• Permissions to create resources in your Azure subscription

Setting Up the Workspace

Azure ML Workspace is a foundational resource where all ML activities are conducted, including.

• Datasets
• Experiments
• Models
• Computing resources

Steps to Create a Workspace

1. Navigate to Azure Machine Learning in the Azure Portal
2. Click "Create a new workspace" and fill in the required details.
   • Subscription: Select your Azure subscription
   • Resource Group: Choose or create a resource group
   • Workspace Name: Enter a unique name for your workspace
   • Region: Select the closest Azure region
   • Storage Account, Key Vault, and Application Insights: These will be automatically created
3. Click "Review + Create", then "Create" to deploy the workspace.

Once created, you can access Azure ML Studio and start configuring your machine learning environment.

Step 2. Ingesting Data into Azure ML Studio.

Data ingestion is a crucial step in machine learning workflows. Azure ML Studio allows users to,

• Upload datasets manually
• Integrate with external storage solutions like Azure Blob Storage, Azure SQL Database, and Azure Data Lake

Uploading Data

1. Open Azure Machine Learning Studio.
2. In the Datasets section, click "Create dataset".
3. Choose a data source (local files, Azure Blob Storage, Azure SQL Database, etc.).
4. Configure the dataset settings (format, delimiter, schema, etc.).
5. Click "Create" to upload the dataset.

Loading Data Using Python SDK

from azureml.core import Workspace, Dataset

# Connect to the workspace
ws = Workspace.from_config()

# Access the registered dataset
dataset = Dataset.get_by_name(ws, name='your_dataset_name')

# Convert to Pandas DataFrame
df = dataset.to_pandas_dataframe()

print(df.head())

Step 3. Setting Up a Basic Experiment.

Experiments in Azure ML allow you to run and track machine learning models. Each experiment logs key metrics and model artifacts, enabling reproducibility and comparison.

Creating an Experiment

• In Azure Machine Learning Studio, navigate to the Experiments section.
• Click "Create new experiment" and give it a name.
• Choose a compute target (local machine or cloud-based virtual machines).
• Define the script to run the model training.

For more details, visit Running and Tracking Experiments in Azure ML

Submitting an Experiment Using Python SDK

from azureml.core import Experiment, ScriptRunConfig

# Create an experiment
experiment = Experiment(workspace=ws, name="basic-ml-experiment")

# Define the training script
config = ScriptRunConfig(
    source_directory=".",
    script="train.py",
    compute_target="local"
)

# Submit the experiment run
run = experiment.submit(config)
run.wait_for_completion(show_output=True)

Sample train.py Script

• from sklearn.model_selection import train_test_split
• from sklearn.ensemble import RandomForestClassifier
• from sklearn.metrics import accuracy_score

import pandas as pd

# Load dataset
df = pd.read_csv('data.csv')

X = df.drop(columns=['target'])
y = df['target']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

# Evaluate model
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

print(f"Model Accuracy: {accuracy:.2f}")

Step 4. Evaluating the Model.

Once the training is complete, you can evaluate the model performance using Azure ML’s built-in metrics tracking.

metrics = run.get_metrics()
print(metrics)

Azure ML Studio provides a visual dashboard where users can inspect.

• Accuracy
• Precision & Recall
• Loss trends

Additionally, users can integrate Azure Machine Learning Interpretability tools to better understand model decisions and refine their approach accordingly.

Conclusion

In this article, we covered,

• Setting up an Azure Machine Learning workspace
• Uploading and loading datasets
• Running a basic experiment with Python
• Training and evaluating a simple model

From here, you can explore AutoML, hyperparameter tuning, and model deployment for more advanced workflows. You can also integrate Azure DevOps and MLOps practices to streamline and automate your ML lifecycle.

Additionally, explore Azure Machine Learning Pipelines to build scalable workflows for continuous integration and deployment of ML models.

For further learning, visit the Azure Machine Learning Documentation