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
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Creating a Workspace Connection
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📌 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
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Step 2: Defining a Training Job
Instead of clicking buttons, you write a training script (train.py), which defines how the model is trained.
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Step 3: Running the Training Job in AzureML Compute Cluster
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📌 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
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Step 2: Deploy the Model as an Endpoint
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📌 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: