A Beginner's Guide to Azure Machine Learning

Azure Machine Learning (Azure ML) is a comprehensive cloud service designed for the machine learning project lifecycle, catering to data scientists, machine learning professionals, and engineers. It integrates seamlessly with the Azure ecosystem, providing a robust platform for building, deploying, and managing machine learning models.

This blog post aims to guide you through the essentials of Azure Machine Learning, from getting started to leveraging advanced features, ensuring you're equipped to utilize this powerful platform efficiently. You can read our separate guide on how to set up and configure Azure if you're just starting out. 

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Overview of Azure Machine Learning

Azure Machine Learning (ML) accelerates the machine learning lifecycle, offering tools for model training, deployment, and management. It supports collaboration, allowing users to share and find assets, resources, and metrics for projects through the Machine Learning studio UI.

This platform is open and interoperable, supporting common Python frameworks like PyTorch, TensorFlow, scikit-learn, and more, as well as R and .NET support.

Azure ML stands out by providing enterprise-grade security, including integration with Azure Virtual Networks, Azure Key Vault, and Azure Container Registry, ensuring that machine learning projects are secure and collaborative.

Who is Azure Machine Learning for?

Azure Machine Learning is designed for individuals and teams focused on integrating MLOps to efficiently deploy machine learning models in secure, auditable production settings.

Data scientists and ML engineers have access to a suite of tools to streamline and enhance their workflows, enabling them to focus on innovation rather than routine tasks.

Application developers benefit from comprehensive tools that facilitate the seamless integration of machine learning models into applications or services, enhancing functionality and user experience.

For organizations operating within the Microsoft Azure cloud ecosystem, there is the added advantage of leveraging Azure's familiar security frameworks and role-based access controls.

A Step-by-Step Guide To Setting Up Your Azure ML Workspace

Creating ML Workspace on Azure

1. Log in to Azure Portal: Start by signing into your Azure Portal account, or create one if necessary.
2. Find Azure Machine Learning: Use the portal's search to locate and select "Machine Learning" among the services.
3. Initiate Workspace Creation: Press "Create" to start setting up a new Machine Learning workspace.
4. Configure Basic Settings:
1. Subscription: Pick your billing Azure subscription.
2. Resource Group: Use an existing group or establish a new one to organize Azure resources.
3. Workspace Name: Assign a unique name to your workspace.
4. Region: Choose a region, preferably close to you or your data sources.
5. Set Resource Details:
1. Storage Account: Assign an existing or new account for project file storage.
2. Key Vault: Select or create a key vault for securely storing secrets.
3. Application Insights: Opt for an Application Insights resource for monitoring.
4. Container Registry: Specify a container registry for any containerized models or services.
6. Review and Deploy: Check your settings, then click "Review + create" for Azure to verify your setup.

Azure Machine Learning Studio

Azure Machine Learning Studio

Azure Machine Learning Studio is one of the core components of Azure Machine Learning (ML). It is a graphical interface integrated development environment (IDE) designed for developing and operationalizing Machine Learning workflows on Azure.

It streamlines the process from data preparation to model deployment, offering a no-code or low-code experience that makes machine learning accessible to a broader range of users, from beginners to seasoned data scientists.

The core of Azure ML Studio's appeal lies in its simplicity and power. It provides a user-friendly, drag-and-drop interface that simplifies the creation, training, and deployment of machine learning models without requiring deep programming knowledge.

Yet, it remains robust enough for complex workflows, offering functionalities like Automated ML (AutoML) and the ML Designer for more controlled, custom pipeline constructions.

ML Studio also integrates seamlessly with the Azure ecosystem, providing tools for monitoring applications and services, securely storing secrets, and managing compute resources. It supports collaboration through shared notebooks and experiments, enhancing the ability of teams to work together effectively on machine learning projects.

For data scientists who prefer coding, ML Studio offers the Azure SDK, which allows for Python code to interact with ML Studio resources and experiments, offering a bridge between the no-code/low-code and code-centric approaches to machine learning.

This flexibility ensures that Azure ML Studio can meet a wide range of needs and preferences, from those who favor visual programming and drag-and-drop simplicity to those who prefer the control and customizability offered by coding.

Azure Automated Machine Learning (AutoML)

Automated ML (AutoML) is one of the core components of Azure Machine Learning. It is known for its ability to automate the selection of algorithms and hyperparameters, streamlining the model training process.

Users simply specify the dataset, the machine learning task (e.g., classification, regression), and some optional parameters, and Azure ML Studio handles the rest, delivering the best-performing model based on the criteria provided.

This not only accelerates the development cycle but also democratizes access to machine learning, enabling users with varying levels of expertise to participate in ML projects.

To use AutoML service in Azure ML is straightforward. Follow these steps:

1. Navigate: Go to "Automated ML" under the "Authoring" section.
2. New Job: Click "+ New automated ML job."
3. Select Data: Choose an existing data asset or create a new one, ensuring it's in tabular form with the target column present.
4. Experiment Setup: Give your experiment a name and choose the compute type for the job.
5. Configure Task: Choose your task type (classification, regression, or forecasting) and configure settings like primary metric and blocked models.
6. Launch: Finalize your configuration and start the experiment.

Developing Machine Learning Models With Code in Azure ML

While Azure ML Studio's no-code, drag-and-drop interface is a major draw for many, it's not always sufficient for every scenario. For cases requiring more customization and control, Azure ML supports development through its SDK, primarily using Python.

This enables data scientists and developers to programmatically construct and manage their machine learning workflows, offering the flexibility to integrate with existing codebases and use advanced machine learning techniques.

Through the SDK, users can automate tasks like data preparation, model training, hyperparameter tuning, and deployment, aligning with more complex project requirements.

When utilizing Azure ML SDK, users can develop and assess machine learning models using standard ML code directly in their local development environment, such as VS Code.

This setup allows for leveraging Azure's computational resources for executing training jobs. The process begins with creating a ml_client as a connection to the Azure workspace, facilitating the management of resources and orchestration of jobs within that environment.

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


# authenticate
credential = DefaultAzureCredential()


SUBSCRIPTION="<SUBSCRIPTION_ID>"
RESOURCE_GROUP="<RESOURCE_GROUP>"
WS_NAME="<AML_WORKSPACE_NAME>"
# Get a handle to the workspace
ml_client = MLClient(
    credential=credential,
    subscription_id=SUBSCRIPTION,
    resource_group_name=RESOURCE_GROUP,
    workspace_name=WS_NAME,
)

To see the full training code, check out the official tutorial.

Conclusion and Next Steps

Azure Machine Learning stands out for its capacity to streamline the machine learning project lifecycle, offering an extensive range of features from model training to deployment. It caters to a broad audience, from data scientists to application developers, providing tools that enhance productivity and innovation.

With its integration into the Azure ecosystem, Azure ML ensures projects are not only advanced but also secure. The platform balances accessibility with depth, offering both a user-friendly interface in ML Studio and comprehensive coding capabilities through the SDK, solidifying its position as a cornerstone in the field of machine learning.

To deepen your understanding of Azure and further enhance your skills, consider exploring the range of Azure-focused courses and certifications available on DataCamp. Whether you're new to Azure or looking to certify your expertise, resources like the Introduction to Azure course, the Azure Fundamentals certification, and the comprehensive Azure Architecture and Services course provide valuable insights.

For a more structured learning path, the Azure Fundamentals track offers a curated journey through key concepts.