PredictionIO: a distributed machine learning server for practical software development

Simon Chan, T. Stone, Kit Pang Szeto, Ka‐Hou Chan
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引用次数: 28

Abstract

One of the biggest challenges for software developers to build real-world predictive applications with machine learning is the steep learning curve of data processing frameworks, learning algorithms and scalable system infrastructure. We present PredictionIO, an open source machine learning server that comes with a step-by-step graphical user interface for developers to (i) evaluate, compare and deploy scalable learning algorithms, (ii) tune hyperparameters of algorithms manually or automatically and (iii) evaluate model training status. The system also comes with an Application Programming Interface (API) to communicate with software applications for data collection and prediction retrieval. The whole infrastructure of PredictionIO is horizontally scalable with a distributed computing component based on Hadoop. The demonstration shows a live example and workflows of building real-world predictive applications with the graphical user interface of PredictionIO, from data collection, algorithm tuning and selection, model training and re-training to real-time prediction querying.
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PredictionIO:用于实际软件开发的分布式机器学习服务器
软件开发人员使用机器学习构建现实世界预测应用程序的最大挑战之一是数据处理框架、学习算法和可扩展系统基础设施的陡峭学习曲线。我们介绍了PredictionIO,这是一个开源的机器学习服务器,它提供了一个循序渐进的图形用户界面,供开发人员(i)评估、比较和部署可扩展的学习算法,(ii)手动或自动调整算法的超参数,以及(iii)评估模型训练状态。该系统还配备了一个应用程序编程接口(API),用于与软件应用程序进行数据收集和预测检索。PredictionIO的整个基础设施是基于Hadoop的分布式计算组件水平扩展的。该演示展示了一个使用PredictionIO图形用户界面构建现实世界预测应用程序的实例和工作流程,从数据收集、算法调优和选择、模型训练和再训练到实时预测查询。
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