How To Deploy LLMs Using Azure ML: A Step-by-Step Guide

Q: What types of endpoints does Azure ML provide for LLM deployment?

Azure ML offers two main deployment options: Real-Time (Online) Endpoints: For low-latency applications like chatbots or APIs. Batch Endpoints: For processing large datasets offline, such as generating embeddings or summarizing documents in bulk.

Quick Summary

This guide explains how to deploy LLMs using Azure ML efficiently. You’ll learn how to set up your workspace, register models like GPT-2 or LLaMA, create a scoring script, configure the environment, and deploy a real-time API that generates text instantly.

Table of Contents

Introduction

Large Language Models (LLMs) have quickly moved from research labs into real-world applications. You can now create powerful AI solutions in days, whether it’s a chatbot that understands customer queries, tools that generate marketing content, or a code assistant that helps developers write code.

But the real challenge is to make these models reliable, scalable, and Production-ready from within your development environment.

That’s where Azure Machine Learning (Azure ML) comes in. It provides a fully managed platform that simplifies deployment, scaling, monitoring, and integration so that you can focus on innovation instead of infrastructure.

In this guide, our Azure developers at Bacancy walk you through the entire process of how to deploy LLMs using Azure ML, from setting up your workspace to making your model accessible as a real-time API for your applications.

How to Deploy an LLM on Azure Machine Learning in 8 Simple Steps

Follow this step-by-step to take a model like GPT-2 from your laptop to a real-time, production-ready API on Azure ML. We’ll set up the workspace, prepare the compute, register the model, create the scoring code and environment, deploy an endpoint, and test it.

Prerequisites

Before you begin, make sure you have the following in place:

An active Azure Subscription: If you don’t have one, create a free account here.
Azure Machine Learning Workspace: Don’t worry; we’ll guide you through creating it in Step 1.
A trained model: For this tutorial, we’ll use the Hugging Face GPT-2 model as an example. However, you can use any other pre-trained LLM if you prefer.
Required software installed locally:
◎ Azure CLI: Install guide
◎ Python 3.8 or later: Download here
◎ Azure ML SDK for Python: Install with:

pip install azure-ai-ml
az extension add -n ml

Step 1: Create Azure ML Workspace

An Azure ML Workspace is your managed environment where you store models, datasets, environments, and deployment endpoints.
1. Sign in to the Azure Portal.
2. In the search bar, type “Machine Learning” and select it.
3. Click Create.

4. Fill in the details:

Subscription: Select your Azure subscription.
Resource Group: Choose an existing one or create a new one.
Workspace Name: For example: llm-workspace (pick something meaningful).
Region: Select the region closest to your users for lower latency.

5. Click Review + create, then Create.
6. Once deployment finishes, open the workspace resource and click Launch Studio to enter the Azure ML Studio interface.

Step 2: Launch Azure ML Studio

Azure ML Studio is the web-based interface where you manage models, datasets, compute resources, and deployments.

1. From your workspace Overview page in the Azure Portal, click Launch Studio.
2. It opens Azure ML Studio in your browser.
3. Sign in with your Azure account if prompted.

Step 3: Create a Compute Cluster

A compute cluster provides the processing power needed to run your model reliably.

1. In Azure ML Studio, go to Manage → Compute.
2. Under Compute clusters, click + New.
3. Configure the cluster:

Name: llm-cluster (or another meaningful name)
VM Size: Standard_NC6 or any GPU-enabled VM for LLM workloads
Min/Max nodes: 0 to 1 (adjust based on workload)

4. Click Create

Tip: CPU VMs can work for very small models, but expect slower response times compared to GPU-enabled instances.

Step 4: Register the Pre-trained LLM Model

Registering your model creates a versioned asset in Azure ML, making it easy to deploy and track updates. You can use a Hugging Face model (GPT-2, LLaMA, or BERT) or link to an OpenAI-based endpoint.

1. In Azure ML Studio, go to “Models”.
2. Click Register model.
3. Fill in the details:

Name: e.g., gpt2
Description: Briefly describe your model
Model source: Upload from local files, link from Blob Storage, or pull from Hugging Face.

Tip: For Hugging Face models, you can download them locally first or use a direct storage link for faster registration.

Bring your AI ideas to life faster and smarter.
Partner with a trusted LLM development company to build, deploy, and scale models that deliver real-world results.

Step 5: Create a Scoring Script (score.py)

The scoring script loads your model once at startup and processes predictions for each incoming request.

from transformers import pipeline
import json

def init():
    global pipe
    pipe = pipeline("text-generation", model="gpt2")

def run(data):
    try:
        input_text = json.loads(data)["text"]
        result = pipe(input_text, max_length=100)
        return result
    except Exception as e:
        return str(e)

Steps in Azure ML Studio:
1. Go to Assets → Files.
2. Upload score.py here, or include it in your deployment package when creating the endpoint.

Tip: Keep your script lightweight and ensure it only loads the model in init(). This prevents unnecessary reloading and speeds up inference.

Step 6: Create an Inference Environment

The inference environment contains all the runtime dependencies your model needs to run in Azure ML.

In Azure ML Studio:
1. Go to Environments → Click Create.
2. Configure:

Name: llm-env
Base: Use a prebuilt Docker image or Conda specification.
Dependencies:

name: llm-env
dependencies:
  - python=3.8
  - transformers
  - torch
  - pip:
      - azureml-defaults

4. Click Create.

Tip: Always pin Python and library versions to avoid unexpected behavior when rebuilding environments in the future.

Step 7: Deploy the Model as a Real-Time Web Service

This step creates a secure HTTPS endpoint that your applications can call for predictions.

In Azure ML Studio:
1. Go to Endpoints → Click Create Endpoint → Real-time endpoint.
2. Configure:

Name: llm-endpoint
Compute type: Kubernetes or AML compute cluster

3. Attach:

Registered model
Inference environment (llm-env)
Scoring script (score.py)

4. Click Deploy.

Tip: Start with a single instance for testing. You can scale out later based on latency and traffic needs.

Step 8: Test the Deployed Endpoint

Make sure the endpoint responds correctly before integrating it into production.

In Azure ML Studio:
1. Open your deployed endpoint.
2. Click Test.
3. Input sample JSON:

{
  "text": "Hello From Bacancy"
}

4. Click Test Invocation and check the response in the output panel.

Tip: Save the endpoint URL and access key; you’ll need them for API calls from external apps.

We hope this Azure Machine Learning LLM tutorial helped you understand how to deploy LLMs efficiently. You can now confidently set up your workspace, register models, and create real-time API endpoints.

Simplify Your LLM Deployment with Azure ML

Deploy LLMs using Azure ML easily, transforming your trained models into secure, scalable, and production-ready APIs in just a few straightforward steps.
From setting up your workspace and registering the model to creating an inference environment and testing the endpoint, this process ensures your LLM is fully functional, secure, and easy to integrate.

You can also opt for end-to-end Azure support services to gain ongoing monitoring, performance tuning, and proactive issue resolution, ensuring your AI deployments remain reliable, optimized, and continuously delivering business value.

Frequently Asked Questions (FAQs)

What types of endpoints does Azure ML provide for LLM deployment?

Azure ML offers two main deployment options:

Real-Time (Online) Endpoints: For low-latency applications like chatbots or APIs.
Batch Endpoints: For processing large datasets offline, such as generating embeddings or summarizing documents in bulk.

How do I ensure optimal performance for LLMs on Azure ML?

Performance can be improved by selecting GPU-enabled compute targets, using optimized model formats (FP16/INT8 quantization), and leveraging multi-GPU setups with frameworks like Hugging Face Accelerate. Azure ML also allows autoscaling to match inference demand and monitor system metrics for efficiency.

Can I deploy my own custom-trained LLM on Azure ML?

If you have a custom-trained LLM in PyTorch, TensorFlow, or ONNX, you can absolutely deploy it on Azure ML. Simply upload your model, set up the inference environment with the required dependencies, and deploy it to a managed endpoint for real-time or batch use.