Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This article provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy and subsequently assess and compare existing standard techniques used at individual stages.

中文翻译：

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编排基于机器学习的物联网应用程序的开发生命周期

机器学习 (ML) 和物联网 (IoT) 是互补的进步：机器学习技术通过智能释放了物联网的潜力，物联网应用越来越多地将传感器收集的数据输入到机器学习模型中，从而利用结果来改进其业务流程和服务。因此，编排包含模型训练和涉及 IoT 应用程序整体开发生命周期的含义的 ML 管道通常会导致复杂的系统集成。本文对基于机器学习的物联网应用程序的开发生命周期进行了全面而系统的调查。我们概述了核心路线图和分类法，随后评估和比较了各个阶段使用的现有标准技术。