webservice Package

Contains functionality for deploying machine learning models as web service endpoints in Azure Machine Learning.

Deploying an Azure Machine Learning model as a web service creates an endpoint and a REST API. You can send data to this API and receive the prediction returned by the model.

You create a web service when you deploy a Model or Image to Azure Container Instances (aci module), Azure Kubernetes Service (aks module) and Azure Kubernetes Endpoint (AksEndpoint), or field-programmable gate arrays (FPGA). Deployment using a model is recommended for most use cases, while deployment using an image is recommended for advanced use cases. Both types of deployment are supported in the classes in this module.

Modules

aci

Contains functionality for deploying machine learning models as web service endpoints on Azure Container Instances.

Azure Container Instances (ACI) is recommended for scenarios that can operate in isolated containers, including simple applications, task automation, and build jobs. For more information about when to use ACI, see Deploy a model to Azure Container Instances.

aks

Contains functionality for deploying machine learning models as web service endpoints on Azure Kubernetes Service.

Azure Kubernetes Service (AKS) is recommended for scenarios where you need full container orchestration, including service discovery across multiple containers, automatic scaling, and coordinated application upgrades.

For more information, see Deploy a model to Azure Kubernetes Service.

container_resource_requirements

Module for describing Container Resource Requirements in Azure Machine Learning.

local

Contains functionality for deploying machine learning models as local web service endpoints.

Deploying to a local web service is recommended for scenarios when you need to quickly deploy and validate your model or you are testing a model that is under development. For more information, see Deploy a model to Notebook VMs.

unknown_webservice

Contains functionality for managing unknown Webservices in Azure Machine Learning.

webservice

Contains functionality for managing models deployed as a web service endpoint in Azure Machine Learning.

This module contains the abstract parent class Webservice, which defines methods for deploying models. A common pattern is to create a configuration object for the specific compute target, and then use the methods of the Webservice class with that configuration object. For example, to deploy to Azure Container Instances, create a AciServiceDeploymentConfiguration object from the deploy_configuration method of the AciWebservice class, and then use one of the deploy methods of the Webservice class. A similar pattern applies for the AksWebservice, AksEndpoint, and LocalWebservice classes.

For an overview of deployment, see Deploy models with Azure Machine Learning.

Classes

AciWebservice

Represents a machine learning model deployed as a web service endpoint on Azure Container Instances.

A deployed service is created from a model, script, and associated files. The resulting web service is a load-balanced, HTTP endpoint with a REST API. You can send data to this API and receive the prediction returned by the model.

For more information, see Deploy a model to Azure Container Instances.

AksEndpoint

Represents a collection of web service versions behind the same endpoint running on Azure Kubernetes Service.

Whereas a AksWebservice deploys a single service with a single scoring endpoint, the AksEndpoint class enables you to deploy multiple web service versions behind the same scoring endpoint. Each web service version can be configured to serve a percentage of the traffic so you can deploy models in a controlled fashion, for example, for A/B testing. The AksEndpoint allows deployment from a model object similar to AksWebservice.

AksWebservice

Represents a machine learning model deployed as a web service endpoint on Azure Kubernetes Service.

A deployed service is created from a model, script, and associated files. The resulting web service is a load-balanced, HTTP endpoint with a REST API. You can send data to this API and receive the prediction returned by the model.

AksWebservice deploys a single service to one endpoint. To deploy multiple services to one endpoint, use the AksEndpoint class.

For more information, see Deploy a model to an Azure Kubernetes Service cluster.

LocalWebservice

Represents a machine learning model deployed as a local web service endpoint.

Deploying web services locally is useful for debugging and testing scenarios.

UnknownWebservice

For internal use only.

This class is used by the Webservice class to get or list service subtypes when the web service was created from a package that wasn't imported, for example, for a service created with the accel package.

Webservice

Defines base functionality for deploying models as web service endpoints in Azure Machine Learning.

Webservice constructor is used to retrieve a cloud representation of a Webservice object associated with the provided Workspace. Returns an instance of a child class corresponding to the specific type of the retrieved Webservice object. The Webservice class allows for deploying machine learning models from either a Model or Image object.

For more information about working with Webservice, see Deploy models with Azure Machine Learning.