{"id":3982,"date":"2023-11-04T23:13:58","date_gmt":"2023-11-04T23:13:58","guid":{"rendered":"http:\/\/localhost:10003\/deploying-a-machine-learning-model-to-azure-machine-learning-service\/"},"modified":"2023-11-05T05:48:25","modified_gmt":"2023-11-05T05:48:25","slug":"deploying-a-machine-learning-model-to-azure-machine-learning-service","status":"publish","type":"post","link":"http:\/\/localhost:10003\/deploying-a-machine-learning-model-to-azure-machine-learning-service\/","title":{"rendered":"Deploying a machine learning model to Azure Machine Learning Service"},"content":{"rendered":"
In today’s data-intensive world, machine learning models can be employed to create value for businesses and individuals alike. With the advent of cloud computing and services such as Azure Machine Learning Service, building and deploying machine learning models has become easier than ever. In this tutorial, we will walk through the steps for deploying a machine learning model to Azure Machine Learning Service.<\/p>\n
Before we begin, ensure you have access to an Azure subscription and have installed the Azure Machine Learning Python SDK.<\/p>\n
An Azure Machine Learning workspace provides a centralized location to manage data, compute resources, and experiments. To create a workspace, follow these steps:<\/p>\n
Before we can deploy a machine learning model, we must first prepare it for deployment. In this example, we will use a pre-trained model for image classification. Specifically, we will use the ResNet50 model pre-trained on the ImageNet dataset.<\/p>\n
A scoring script is used to output the predictions made by the machine learning model. In this example, we will output the predicted class label and the probability for each prediction.<\/p>\n
An environment file specifies the dependencies required by the scoring script and the compute environment on which the script will run. In this example, we will use a Python 3.6 environment.<\/p>\n
the Python version and the necessary dependencies. In this example, we will require tensorflow, onnxruntime, and numpy.<\/p>\n
name: myenv\ndependencies:\n- python=3.6\n- pip\n- tensorflow\n- onnxruntime\n- numpy\n<\/code><\/pre>\nStep 5: Define the deployment configuration<\/h2>\n
The deployment configuration specifies the compute target, entry script, environment file, and other necessary settings for deploying the machine learning model. In this example, we will use the Azure Kubernetes Service (AKS) as the compute target.<\/p>\n
\n- Define the deployment configuration using the Azure Machine Learning SDK.<\/li>\n<\/ol>\n
from azureml.core.model import InferenceConfig, Model\nfrom azureml.core.webservice import AksWebservice\nfrom azureml.core.compute import AksCompute\nfrom azureml.core.compute_target import ComputeTargetException\n\naks_name = \"myaks\"\naks_resource_group = \"myresourcegroup\"\naks_location = \"westus2\"\nmodel_name = \"myonnxmodel\"\ndeployment_config = AksWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n<\/code><\/pre>\n\n- Define the AKS compute target.<\/li>\n<\/ol>\n
try:\n aks_target = AksCompute(workspace=ws, name=aks_name)\n print(\"Found existing AKS service:\", aks_name)\nexcept ComputeTargetException:\n print(\"Creating a new AKS service...\")\n prov_config = AksCompute.provisioning_configuration(location=aks_location, resource_group=aks_resource_group)\n aks_target = ComputeTarget.create(workspace=ws, name=aks_name, provisioning_configuration=prov_config)\n aks_target.wait_for_completion(show_output=True)\n<\/code><\/pre>\n\n- Define the input and output data types for the scoring script.<\/li>\n<\/ol>\n
input_data = {\n 'image': 'numpy.ndarray'\n}\n\noutput_data = {\n 'output': 'numpy.ndarray'\n}\n<\/code><\/pre>\n\n- Define the inference configuration using the scoring script, environment file, and input and output data types.<\/li>\n<\/ol>\n
inference_config = InferenceConfig(\n source_directory='.',\n entry_script='score.py',\n environment_file='myenv.yml',\n input_data=input_data,\n output_data=output_data\n)\n<\/code><\/pre>\n\n- Retrieve the ONNX model from the Azure Machine Learning workspace.<\/li>\n<\/ol>\n
model = Model(ws, name=model_name, version=1)\n<\/code><\/pre>\nStep 6: Deploy the machine learning model<\/h2>\n
Now that we have prepared the machine learning model, scoring script, environment file, and deployment configuration, we can deploy the model to Azure Machine Learning Service using the following command:<\/p>\n
aks_service = Model.deploy(ws, models=[model], inference_config=inference_config, deployment_config=deployment_config, deployment_target=aks_target)\naks_service.wait_for_deployment(show_output=True)\nprint(aks_service.state)\n<\/code><\/pre>\nThe above command will deploy the machine learning model to the AKS compute target and output its deployment state.<\/p>\n
Conclusion<\/h2>\n
In this tutorial, we have walked through the steps for deploying a machine learning model to Azure Machine Learning Service. By following these steps, you can easily deploy a machine learning model to Azure and make it available for consumption by other applications and services.<\/p>\n","protected":false},"excerpt":{"rendered":"
Introduction In today’s data-intensive world, machine learning models can be employed to create value for businesses and individuals alike. With the advent of cloud computing and services such as Azure Machine Learning Service, building and deploying machine learning models has become easier than ever. In this tutorial, we will walk Continue Reading<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[1],"tags":[692,689,691,690,212,688],"yoast_head":"\nDeploying a machine learning model to Azure Machine Learning Service - Pantherax Blogs<\/title>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\t\n\t\n\t\n