论文标题
Ednaml：可再现深度学习的声明性API和框架
EdnaML: A Declarative API and Framework for Reproducible Deep Learning
论文作者
论文摘要
机器学习已成为文本，图像，视频以及音频处理和发电的最新进展的基石。大多数生产系统在部署和培训期间都涉及多种模型，每种模型都有各种调整的超参数。此外，由于其在创建可持续的高质量分类器方面的重要性，ML管道的数据收集和处理方面正在获得越来越多的兴趣。我们提出了Ednaml，这是一个具有声明性API的框架，可重复学习。 Ednaml提供了可以手动组成的低级构建块，以及高级管道编排API，以自动化数据收集，数据处理，分类器培训，分类器部署和模型监视。我们的分层API允许用户在高级组件摘要上管理ML管道，同时提供了通过构建块修改其任何部分的灵活性。我们介绍了EDNAML的ML管道的几个示例，包括一个大规模的假新闻标签和分类系统，其中包括由Ednaml管理的六个子管道。
Machine Learning has become the bedrock of recent advances in text, image, video, and audio processing and generation. Most production systems deal with several models during deployment and training, each with a variety of tuned hyperparameters. Furthermore, data collection and processing aspects of ML pipelines are receiving increasing interest due to their importance in creating sustainable high-quality classifiers. We present EdnaML, a framework with a declarative API for reproducible deep learning. EdnaML provides low-level building blocks that can be composed manually, as well as a high-level pipeline orchestration API to automate data collection, data processing, classifier training, classifier deployment, and model monitoring. Our layered API allows users to manage ML pipelines at high-level component abstractions, while providing flexibility to modify any part of it through the building blocks. We present several examples of ML pipelines with EdnaML, including a large-scale fake news labeling and classification system with six sub-pipelines managed by EdnaML.
