作为可重用微服务的机器学习

Marc-Oliver Pahl, Markus Loipfinger
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引用次数: 10

摘要

机器学习最近正在成为一种通用的解决问题的工具。然而,将机器学习(ML)实现到应用程序中是困难的,耗时的,并且需要专业知识。我们将机器学习封装为一个面向数据的微服务,可以简单地用于混合具有机器学习功能的应用程序。为了说明这种方法,我们确定了与物联网(IoT)相关的三种机器学习算法:前馈神经网络(FFNN)、深度相信网络(DBN)和循环神经网络(RNN)。我们分析了这些算法的特征属性,并将其建模为可动态链接的REST ML服务模块的配置。我们的方法严格地将算法实现与其配置分开。它允许对各种ML算法进行简单的扩展。遵循面向服务的设计,我们将神经网络的训练作为一个单独的模块来实现。我们评估了我们的解决方案的性能如何与直接编程所选择的TensorFlow库进行比较。我们的方法通过支持可执行文件和配置的重用和共享,极大地促进了基于ml的数据分析的实现。它使快速原型和探索使用机器学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning as a reusable microservice
Machine Learning is recently becoming a universal problem solving tool. However, implementing machine learning (ML) into applications is difficult, time intense, and requires expert knowledge. We encapsulate machine learning as a data­oriented microservice that can simply be used to mash up applications with machine learning capabilities. To illustrate the approach we identify three machine learning algorithms that are relevant for the Internet of Things (IoT): Feed-Forward Neural Networks (FFNN), Deep Believe Networks (DBN), and Recurrent Neural Networks (RNN). We analyze those algorithm's characteristic properties and model them as configurations for dynamically linkable REST ML service modules. Our approach strictly separates the algorithm implementation from its configu­ration. It allows a simple extension with diverse ML algorithms. Following a service oriented design, we implement the training of our neural networks as a separate module. We evaluate how the performance of our solution compares to directly programming the chosen TensorFlow library. Our approach facilitates the implementation of ML-based data analytics significantly by enabling reuse and sharing of executables and configurations. It enables rapid prototyping and an explorative use of ML.
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