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 its 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.
翻译:在数据库内部署机器学习算法是一项挑战,因为现代ML算法的计算足迹各异,而且数据库技术各有其限制性的语法。我们引入了基于Apache Spark的微服务管弦框架,将数据库业务扩展至包括网络服务原始人。我们的系统可以通过数百台机器协调网络服务,充分利用集群、线和不同步平行的平行关系。我们利用这个框架,为语言、视觉、搜索、异常探测和文本分析等智能服务提供大规模客户服务。这使得用户能够将即时使用的情报与任何与Apache Spark连接器的数据储存结合起来。为了消除网络通信的大部分间接费用,我们还引入了我们结构的低纬度集装箱化版本。最后,我们证明我们所调查的服务在各种基准上具有竞争力,并展示了这个框架的两种应用,以创建智能搜索引擎和实时自动竞赛分析系统。