大数据分析系统基准测试教程

Todor Ivanov, Rekha Singhal
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引用次数: 0

摘要

大数据技术和更快的计算系统的扩散导致基于人工智能的解决方案在我们的生活中无处不在。需要了解如何对用于构建基于人工智能的解决方案的系统进行基准测试,这些解决方案具有复杂的预处理、统计分析、机器学习和深度学习管道,以构建预测模型。解决方案架构师、工程师和研究人员可以根据期望的性能需求使用开源技术或专有系统。性能指标可以是数据预处理时间、模型训练时间和模型推理时间。我们没有看到一个单一的基准可以回答解决方案架构师和研究人员的所有问题。本教程涵盖了相关大数据和分析基准的实践和研究问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tutorial on Benchmarking Big Data Analytics Systems
The proliferation of big data technology and faster computing systems led to pervasions of AI based solutions in our life. There is need to understand how to benchmark systems used to build AI based solutions that have a complex pipeline of pre-processing, statistical analysis, machine learning and deep learning on data to build prediction models. Solution architects, engineers and researchers may use open-source technology or proprietary systems based on desired performance requirements. The performance metrics may be data pre-processing time, model training time and model inference time. We do not see a single benchmark answering all questions of solution architects and researchers. This tutorial covers both practical and research questions on relevant Big Data and Analytics benchmarks.
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