Introduction to Azure AI Machine Learning

Published: 2023-10-27 Author: Microsoft Docs Team Tags: Azure, AI, Machine Learning, MLops

Azure Machine Learning (Azure ML) is a cloud-based service that you can use to train, deploy, manage, and track machine learning models.

It provides an integrated environment where you can leverage the speed and scale of Azure cloud to build and manage your machine learning projects.

What is Azure Machine Learning?

Azure ML offers a comprehensive suite of tools and services designed to streamline the entire machine learning lifecycle. This includes:

Data Preparation: Tools to connect to data sources, clean, transform, and feature engineer your data.
Model Training: Support for various training frameworks like TensorFlow, PyTorch, scikit-learn, and MLflow. You can train models using automated ML (AutoML) or by writing custom code.
Model Deployment: Easily deploy your trained models as web services (REST APIs) for real-time inference or in batch for large-scale predictions.
MLOps: Capabilities for managing, monitoring, and versioning your models and pipelines to ensure reproducible and scalable machine learning operations.
Responsible AI: Tools and guidance to help you build and deploy AI systems that are fair, reliable, safe, transparent, and privacy-preserving.

Key Components and Concepts

Understanding the core components of Azure ML is crucial for effective utilization:

Workspaces

An Azure ML workspace is the top-level resource for Azure Machine Learning. It provides a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace keeps track of all jobs, experiments, models, and other assets.

Compute Resources

Azure ML allows you to create and manage various compute targets for training and inference, including:

Compute Instances: Managed cloud-based workstations for development.
Compute Clusters: Scalable clusters of VMs for training ML models.
Inference Clusters (AKS/ACI): Kubernetes clusters or Azure Container Instances for deploying models.

Datastores and Datasets

Datastores securely connect to storage services in Azure (like Azure Blob Storage or Azure Data Lake Storage). Datasets are pointers to data in your datastores, enabling efficient data access and management within your ML experiments.

Experiments and Jobs

An experiment is a logical grouping of multiple jobs. A job represents a single run of your training script or a data processing step. Azure ML tracks metrics, parameters, and outputs for each job, allowing for easy comparison and reproducibility.

Models

A model in Azure ML is a trained machine learning model that you register in your workspace. Once registered, models can be versioned and deployed.

Environments

Environments define the Python environment, Conda packages, pip packages, environment variables, and Docker settings needed to run your training script or inference code.

Getting Started with Azure ML

To begin using Azure ML, you'll typically follow these steps:

Create an Azure ML Workspace: This can be done through the Azure portal, Azure CLI, or Python SDK.
Set up your Development Environment: Use Azure ML Studio (a web portal), VS Code with the Azure ML extension, or Jupyter notebooks.
Connect to Data: Register your data sources as Datastores and create Datasets.
Create Compute Targets: Provision compute resources for training and deployment.
Train a Model: Use AutoML or write custom training scripts.
Register and Deploy the Model: Make your model available for inference.

Example: Simple Training Script

Here's a basic example of a Python script that could be run as an Azure ML job using scikit-learn:

                    
import os
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import pandas as pd
from azureml.core import Run

# Get the current run
run = Run.get_context()

# Load data (assuming data is available in the specified path)
# In a real scenario, you'd load data from an Azure ML Dataset
data_path = 'azureml://datastores/my_datastore/paths/my_data.csv'
df = pd.read_csv(data_path)

# Prepare data
X = df[['feature1', 'feature2']]
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 = LogisticRegression()
model.fit(X_train, y_train)

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

# Log metrics to Azure ML
run.log('accuracy', accuracy)
print(f'Model Accuracy: {accuracy}')

# Save the trained model
os.makedirs('outputs', exist_ok=True)
with open('outputs/model.pkl', 'wb') as f:
    import joblib
    joblib.dump(model, f)

print('Model training complete and saved to outputs/model.pkl')
                    
                

This script demonstrates loading data, training a simple logistic regression model, logging the accuracy, and saving the model artifact. Azure ML will handle the execution of this script on your chosen compute target and log the results.

Azure Machine Learning is a powerful platform that empowers data scientists and developers to build, deploy, and manage sophisticated AI solutions at scale. Whether you're new to machine learning or an experienced practitioner, Azure ML offers the tools and flexibility you need to succeed.