OnDemandMLflowTrainAndServe:使用Azure Databricks和MLflow的按需训练和服务的机器学习模型的解决方案-源码

上传者: 42101384 | 上传时间: 2021-01-30 05:08:29 | 文件大小: 211KB | 文件类型: ZIP
使用MLflow服务按需机器学习模型 使用和的按需训练和服务的机器学习模型的解决方案。 那里的大多数文档和样本通常会向您展示如何采用一种特定的ML模型并将其引入具有生产规模的生产环境中。 在此示例解决方案中,我们尝试提供一种方法来同时缩放系统中现有的不同模型的数量,而不是针对将要回答的推理请求的数量缩放一个特定的模型。 它是根据实际用例进行概括的,在该用例中,最终用户的行为需要对模型进行即时训练,然后根据其规格进行小规模推理。 此存储库中的代码允许创建MLflow项目的数据科学家使用不同的参数对其进行测试,然后再根据经过训练的模型为模型提供预测结果。 运行此示例时,我们假设一个笔记本已经在Azure Databricks中加载,并且该笔记本正在使用MLflow来存储和记录实验。 在此存储库中,有2个(基于MLflow提供的示例),您可以用来入门。 建筑 该解决方案包括在上部署的3个。 在此示例中,服务通过REST API进行通信。 是解决方案的入口点,可在发出火车模型请求和运行模型以接收预测结果之间进行导航。 管理对Databricks的请求-启动集群并运行笔记本来训练ML

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