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Moving Beyond the UI: Why Code-First?

When working with Azure Machine Learning (AzureML), you have two primary options: a drag-and-drop UI (like ML Designer & AutoML) or a fully code-driven approach using Python and the AzureML SDK. While UI-based solutions make things accessible, they often lack the flexibility and control that developers, data scientists, and MLOps engineers need.

A code-first approach provides:

✔ Full automation & scripting for repeatable experiments.
✔ Customization beyond what UI tools allow.
✔ Seamless integration into CI/CD workflows for ML models.
✔ The ability to scale experiments across multiple compute clusters.

By coding everything from data ingestion to model deployment, you ensure full reproducibility and scalability. This is crucial for enterprise applications where ML models must be maintained, updated, and monitored in production environments. Additionally, writing scripts allows for easy debugging, version control, and tracking of hyperparameter tuning experiments, which can be difficult to manage manually in UI-based workflows.

Let’s explore how to set up, train, and deploy ML models entirely with code in AzureML.

Setting Up an AzureML Workspace (Using Python)

To start coding with AzureML, you first need to create or connect to an AzureML workspace. The workspace acts as a central hub where you manage datasets, compute resources, and ML models.

Installing the AzureML SDK

Creating a Workspace Connection

📌 What’s happening? This connects your Python environment to AzureML, allowing you to execute commands programmatically.

Training a Model: Code vs. UI

In UI-based training (AutoML or ML Designer), you select models and hyperparameters manually. With code-first training, you define everything programmatically, giving full control over the ML pipeline.

Step 1: Uploading a Dataset to AzureML

Step 2: Defining a Training Job

Instead of clicking buttons, you write a training script (train.py), which defines how the model is trained.

Step 3: Running the Training Job in AzureML Compute Cluster

📌 Key Insight: This submits the training script to AzureML’s compute cluster, where it runs automatically.

Deploying the Trained Model as an API

Once the model is trained, deployment can also be done fully via code—no UI needed. This means you can seamlessly integrate the deployment process into automated workflows, ensuring consistent and repeatable model releases. Instead of manually configuring endpoints, compute resources, and dependencies through the Azure ML UI, you can define everything programmatically, allowing for version control, parameter tuning, and batch deployments at scale. Additionally, deploying via code enables continuous integration (CI/CD) pipelines, where models can be updated dynamically based on performance monitoring or retraining schedules, reducing the need for manual intervention.

Step 1: Register the Model

Step 2: Deploy the Model as an Endpoint

📌 Final Step: Once deployed, you get a REST API URL for real-time predictions.

Why Go Code-First Instead of UI?

Final Thoughts: When to Use Code-First?

A code-first approach to AzureML is best suited for scenarios where machine learning models require scalability, automation, and precise customization. It empowers developers and data scientists to work efficiently without UI limitations, making it the preferred choice for enterprise-level AI applications.

Use Code-First If:

✔ You need full control over training, deployment, and pipeline automation.

Writing scripts ensures a fully reproducible ML pipeline where every step is automated and version-controlled.

✔ Your models require advanced customization (hyperparameter tuning, custom training loops).

With code-first, you can apply fine-tuned configurations that aren’t available in UI-based solutions, allowing for better optimization.

✔ You’re integrating ML into DevOps/MLOps workflows.

Code-based ML development integrates seamlessly with CI/CD pipelines, making it easier to deploy, monitor, and retrain models continuously.

✔ You want to automate training jobs with CI/CD pipelines.

Automating workflows with scripts ensures that new data triggers retraining and deployment without manual intervention, increasing efficiency in production environments.

🔗 Further Learning:

Revolutionizing ML with Automated Machine Learning

Machine learning is powerful, but it often requires data scientists to manually test and tune models—a process that can take weeks or even months. What if this entire process could be automated, allowing AI to do the heavy lifting? That’s exactly what Automated ML (AutoML) in Azure Machine Learning (AzureML) does.

With AutoML, you can:

✅ Automatically select the best ML model for your dataset.
✅ Optimize hyperparameters without manual intervention.
✅ Deploy models quickly with minimal coding.

Let’s explore how AutoML in AzureML simplifies machine learning workflows and how you can start using it today.

Getting Started with AutoML in AzureML

Unlike traditional ML development, where you manually build and train models, AutoML in AzureML allows you to train models through a no-code UI inside the AzureML workspace.

Here’s how it works:

Upload your dataset (CSV, Parquet, or linked Azure storage).
Define your prediction goal (classification, regression, or forecasting).
Let AutoML analyze multiple models and find the best one.
Deploy the selected model with just a few clicks.

Unlike traditional model training, where you need to manually choose algorithms and tune hyperparameters, AutoML automates this selection, providing results faster and more accurately.

How Does AutoML Work?

Behind the scenes, AutoML uses machine learning pipelines to:

Preprocess data (handling missing values, encoding categorical variables).
Select and test multiple ML models (XGBoost, LightGBM, Neural Networks, etc.).
Optimize hyperparameters using Bayesian optimization.
Provide explainability metrics, so you know why a model was chosen.

Once AutoML completes the training process, it ranks the models based on key performance metrics like accuracy, RMSE (root mean squared error), or AUC (area under the curve), depending on the task.

AutoML in Action: Training a Model

To get hands-on, let’s walk through training a model using AutoML in AzureML.

1️⃣ Upload Your Dataset

Navigate to the AzureML workspace → Open the Automated ML tab → Click + New AutoML Run.

Select your dataset (e.g., customer churn data, loan default prediction, or sales forecasting).

2️⃣ Choose a Target Variable

Select the column you want to predict (e.g., “Churn” for customer churn prediction). AzureML automatically detects whether it’s a classification, regression, or forecasting task.

3️⃣ Configure Compute

Choose a compute instance or cluster. AzureML will scale resources automatically, running multiple experiments in parallel.

4️⃣ Start Training

Click Run, and AutoML begins testing multiple models. You can monitor progress in real-time inside the workspace.

Evaluating the Best Model

Once training is complete, AutoML ranks the models based on performance metrics. Click on the best-performing model to:

View model insights (feature importance, confusion matrix, etc.).
Download logs and results for further analysis.
Deploy the model directly as an endpoint in Azure.

📝 Pro Tip: You can export the best model to a Jupyter Notebook for further fine-tuning.

Deploying Your AutoML Model

Once you’re satisfied with the model, deploying it as an API endpoint is just a few clicks away.

Click Deploy Model → Choose Real-Time Endpoint.
Select an Azure container instance or Kubernetes service.
AzureML generates a REST API endpoint for real-time predictions.

Now, you can send live data to this endpoint via a simple API call.

Why Use AutoML Instead of Traditional ML?

Traditional machine learning requires extensive expertise in model selection, feature engineering, and tuning hyperparameters. AutoML in AzureML simplifies this process, making ML more accessible to:

Data analysts & business users (No coding required!).
Developers who want to integrate ML without deep expertise.
Machine learning engineers looking to accelerate model training.

🔗 Azure Docs:

Final Thoughts & Next Steps

Azure Automated Machine Learning (AutoML) is a game-changer, allowing users to build and deploy ML models quickly. Whether you’re predicting customer behavior, forecasting sales, or analyzing medical data, AutoML does the hard work for you.

🔄 Next Steps: Try AutoML on a real-world dataset, and see how quickly you can deploy a model! 🚀

Tag: AzureML

Code-First Programming with AzureML: A Developer’s Guide