How to Deploy LLMs Using Azure Machine Learning (Step-by-Step Guide)

• Pradip
• 12 Nov, 2025
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How to Deploy LLMs Using Azure Machine Learning (Step-by-Step Guide)

How to Deploy LLMs Using Azure Machine Learning (Azure ML)

Large Language Models (LLMs) like GPT, Llama, or Falcon are transforming industries with their ability to understand and generate human-like text. But deploying these massive models for real-world use can be challenging. Azure Machine Learning (Azure ML) simplifies this process by providing a secure, scalable, and fully managed environment to deploy, manage, and monitor LLMs efficiently.

Step 1: Set Up the Azure ML Environment

Before deploying any model, ensure you have:

• An Azure subscription

• An Azure Machine Learning workspace

• Azure CLI and Azure ML SDK installed

Create a new Azure ML workspace:

You can create a workspace via the Azure Portal or CLI:

Configure the workspace in Python:

from azureml.core import Workspace

 ws = Workspace.from_config() print(ws.name, ws.location, ws.resource_group )

Step 2: Choose or Import Your LLM

You can deploy:

• Prebuilt models from Azure AI Model Catalog (like GPT, Falcon, etc.)

• Custom models you’ve fine-tuned locally or on another platform.

Example: importing a model from the Hugging Face Hub:

from azureml.core import Model

model = Model.register(workspace=ws,
model_name="falcon-7b",
model_path="./models/falcon-7b")

Step 3: Create an Inference Environment

You’ll need a Docker environment with all dependencies (PyTorch, Transformers, etc.). Azure ML makes this easy with curated environments.

from azureml.core.environment import Environment

env = Environment.from_conda_specification(
name="llm-env",
file_path="environment.yml"
)

name: llm-env
dependencies:
- python=3.9
- pip:
- torch
- transformers
- azureml-defaults

Step 4: Define the Inference Script

Create a score.py file that loads the model and handles incoming requests.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

def init():
global model, tokenizer
model_name = "tiiuae/falcon-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def run(raw_data):
prompt = raw_data.get("prompt", "")
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
return tokenizer.decode(outputs[0], skip_special_tokens=True)

Step 5: Deploy the Model as a Web Service

Now create an inference configuration and deploy it to Azure Kubernetes Service (AKS) or Azure Container Instances (ACI).

from azureml.core.model import InferenceConfig
from azureml.core.webservice import AciWebservice

inference_config = InferenceConfig(entry_script="score.py", environment=env)

deployment_config = AciWebservice.deploy_configuration(cpu_cores=4, memory_gb=16)

service = Model.deploy(workspace=ws,
name="falcon7b-service",
models=[model],
inference_config=inference_config,
deployment_config=deployment_config)
service.wait_for_deployment(show_output=True)

Step 6: Test the Deployment

Once the deployment completes successfully, test your endpoint:

import requests, json

scoring_uri = service.scoring_uri
input_data = json.dumps({"prompt": "Explain Azure Machine Learning in simple terms."})

headers = {"Content-Type": "application/json"}
response = requests.post(scoring_uri, data=input_data, headers=headers)
print(response.text)

Step 7: Monitor and Scale

Azure ML provides monitoring for:

• Endpoint performance (latency, throughput)

• Logs and metrics via Application Insights

• Autoscaling options for production workloads

You can also integrate with Azure Monitor to automate alerts and scaling based on load.

Best Practices for LLM Deployment on Azure ML

• Use GPU-based compute clusters for large models (like Standard_NC6s_v3)
• Store model artifacts in Azure Blob Storage for fast access
• Use Azure Key Vault for securing API keys or model credentials
• Integrate Azure Front Door or API Management for production-grade routing and security
• Use model versioning to manage updates without downtime

Conclusion

Deploying LLMs using Azure Machine Learning streamlines the process of taking AI from experimentation to production. Whether you’re working with OpenAI models, Hugging Face transformers, or your own fine-tuned LLM, Azure ML provides the flexibility, scalability, and enterprise security needed for real-world AI deployment.

By following the steps above, you can build, deploy, and scale your LLM applications with confidence.

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