Deploying Machine Learning (ML) algorithms within databases is a challenge due to the varied computational footprints of modern ML algorithms and the myriad of database technologies each with their own restrictive syntax. We introduce an Apache Spark-based micro-service orchestration framework that extends database operations to include web service primitives. Our system can orchestrate web services across hundreds of machines and takes full advantage of cluster, thread, and asynchronous parallelism. Using this framework, we provide large scale clients for intelligent services such as speech, vision, search, anomaly detection, and text analysis. This allows users to integrate ready-to-use intelligence into any datastore with an Apache Spark connector. To eliminate the majority of overhead from network communication, we also introduce a low-latency containerized version of our architecture. Finally, we demonstrate that the services we investigate are competitive on a variety of benchmarks, and present two applications of this framework to create intelligent search engines, and real time auto race analytics systems.

中文翻译：

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大规模智能微服务

由于现代 ML 算法的计算足迹各不相同，以及无数数据库技术都有自己的限制性语法，因此在数据库中部署机器学习 (ML) 算法是一项挑战。我们引入了一个基于 Apache Spark 的微服务编排框架，该框架将数据库操作扩展为包括 Web 服务原语。我们的系统可以在数百台机器上编排 Web 服务，并充分利用集群、线程和异步并行性。使用该框架，我们为语音、视觉、搜索、异常检测和文本分析等智能服务提供大规模客户端。这允许用户使用 Apache Spark 连接器将即用型智能集成到任何数据存储中。为了消除网络通信的大部分开销，我们还介绍了我们架构的低延迟容器化版本。最后，我们证明我们调查的服务在各种基准测试中都具有竞争力，并展示了该框架的两个应用程序来创建智能搜索引擎和实时汽车比赛分析系统。