End-to-End Machine Learning with Apache AsterixDB

Proceedings of the Second Workshop on Data Management for End-to-End Machine Learning. Workshop on Data Management for End-to-End Machine Learning (2nd : 2018 : Houston, Tex.) Pub Date : 2018-06-15 DOI:10.1145/3209889.3209894

Wail Y. Alkowaileet, Sattam Alsubaiee, M. Carey, Chen Li, H. Ramampiaro, Phanwadee Sinthong, Xikui Wang

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引用次数: 11

Abstract

Recent developments in machine learning and data science provide a foundation for extracting underlying information from Big Data. Unfortunately, current platforms and tools often require data scientists to glue together and maintain custom-built platforms consisting of multiple Big Data component technologies. In this paper, we explain how Apache AsterixDB, an open source Big Data Management System, can help to reduce the burden involved in using machine learning algorithms in Big Data analytics. In particular, we describe how AsterixDB's built-in support for user-defined functions (UDFs), the availability of UDFs in data ingestion pipelines and queries, and the provision of machine learning platform and notebook inter-operation capabilities can together enable data analysts to more easily create and manage end-to-end analytical dataflows.

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端到端机器学习与Apache AsterixDB

机器学习和数据科学的最新发展为从大数据中提取底层信息提供了基础。不幸的是，当前的平台和工具通常需要数据科学家粘合在一起，并维护由多种大数据组件技术组成的定制平台。在本文中，我们解释了Apache AsterixDB，一个开源的大数据管理系统，如何帮助减少在大数据分析中使用机器学习算法所带来的负担。特别是，我们描述了AsterixDB对用户定义函数(udf)的内置支持，udf在数据摄取管道和查询中的可用性，以及机器学习平台和笔记本互操作功能的提供如何使数据分析师能够更轻松地创建和管理端到端分析数据流。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the Second Workshop on Data Management for End-to-End Machine Learning. Workshop on Data Management for End-to-End Machine Learning (2nd : 2018 : Houston, Tex.)

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