将模型部署到生产中,使这些模型在制定业务决策方面能够发挥积极作用Deploy models to production to play an active role in making business decisions

生产部署可让模型在企业中发挥积极作用。Production deployment enables a model to play an active role in a business. 所部署模型提供的预测可用于业务决策。Predictions from a deployed model can be used for business decisions.

生产平台Production platforms

可通过多种方法和平台将模型投入生产。There are various approaches and platforms to put models into production. 下面是几个选项:Here are a few options:

备注

在部署之前,必须确保模型的延迟评分够低,使模型可在生产环境中使用。Prior to deployment, one has to insure the latency of model scoring is low enough to use in production.

备注

对于使用 Azure 机器学习工作室的部署,请参阅部署 Azure 机器学习 Web 服务For deployment using Azure Machine Learning Studio, see Deploy an Azure Machine Learning web service.

A/B 测试A/B testing

如果在生产环境中部署了多个模型,执行 A/B 测试来比较模型的性能可能很有用。When multiple models are in production, it can be useful to perform A/B testing to compare performance of the models.

后续步骤Next steps

我们还提供了相应的演练,用于演示具体方案的操作过程的所有步骤。Walkthroughs that demonstrate all the steps in the process for specific scenarios are also provided. 示例演练一文列出了相关步骤并以缩略图说明的形式提供了链接。They are listed and linked with thumbnail descriptions in the Example walkthroughs article. 这些演练演示如何将云、本地工具和服务合并到工作流或管道中,以创建智能应用程序。They illustrate how to combine cloud, on-premises tools, and services into a workflow or pipeline to create an intelligent application.