Deploy a model to an online endpoint

To deploy a model to an online endpoint in the context of the Designing and Implementing a Data Science Solution on Azure exam, you need to follow a series of steps. By deploying a model to an online endpoint, you can make predictions using your trained model in real-time. In this article, we will explore the process of deploying a model on Azure using Azure Machine Learning service.

Step 1: Set up Azure Machine Learning workspace

To begin, create an Azure Machine Learning workspace in your Azure portal. This workspace will serve as a centralized location to manage your machine learning resources.

Step 2: Prepare your model for deployment

Before you can deploy your model, you need to package it with any dependencies it may have. This can be achieved using Python’s virtual environments. Start by creating a virtual environment with the necessary dependencies and activate it.

python
python -m venv myenv
source myenv/bin/activate

Next, install the required packages using pip.

python
pip install azureml-core azureml-sdk[notebooks,automl] azureml-tensorboard azureml-widgets

Step 3: Register your model in Azure Machine Learning

To deploy your model, you need to register it in the Azure Machine Learning workspace. This step allows you to version and track your models.

python
from azureml.core import Workspace, Model

# Load or create Azure Machine Learning workspace
ws = Workspace.get(name=’your_workspace_name’)

# Register your model
model = Model.register(model_path=’model.pkl’, model_name=’my_model’, workspace=ws)

Step 4: Create the scoring script

The scoring script is a Python script that defines how your model should be loaded and used to make predictions. It typically includes the necessary preprocessing and post-processing steps.

Create a new Python file named score.py and define a function called init that loads and initializes your model. Also, define a function called run that uses the initialized model to make predictions.

python
import json
import numpy as np
import os
from azureml.core.model import Model

def init():
global model
model_path = Model.get_model_path(‘my_model’)
model = load_model(model_path)

def run(raw_data):
# Convert the raw data to JSON
data = json.loads(raw_data)[“data”]

# Perform any necessary preprocessing here
processed_data = preprocess(data)

# Use the model to make predictions
predictions = model.predict(processed_data)

# Perform any necessary post-processing here
postprocessed_predictions = postprocess(predictions)

return json.dumps({“result”: postprocessed_predictions})

Step 5: Define the inference configuration

The inference configuration specifies the environment required to host the model. It includes the scoring script and the conda dependencies required to run the script.

python
from azureml.core.environment import Environment
from azureml.core.model import InferenceConfig

# Create a new environment
env = Environment(‘my_environment’)
env.python.conda_dependencies.add_pip_package(‘numpy’)
env.python.conda_dependencies.add_pip_package(‘scikit-learn’)

# Specify the dependencies for the scoring script
inference_config = InferenceConfig(entry_script=’score.py’, environment=env)

Step 6: Deploy the model to an online endpoint

Finally, you can deploy the model to an online endpoint using Azure Container Instances (ACI) or Azure Kubernetes Service (AKS).

python
from azureml.core.webservice import AciWebservice

deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)

# Deploy the model to ACI
service = Model.deploy(ws, ‘my-service’, [model], inference_config, deployment_config)
service.wait_for_deployment(show_output=True)

Congratulations! You have successfully deployed your model as an online endpoint. You can now make predictions by sending HTTP POST requests to the endpoint with the necessary input data.

In this article, we explored the process of deploying a model to an online endpoint on Azure using Azure Machine Learning service. Remember to refer to the official Microsoft documentation for more detailed instructions and additional features for deploying and managing your models.