Development of machine learning (ML) workflows is a tedious process of
iterative experimentation: developers repeatedly make changes to workflows
until the desired accuracy is attained. We describe our vision for a
"human-in-the-loop" ML system that accelerates this process: by intelligently
tracking changes and intermediate results over time, such a system can enable
rapid iteration, quick responsive feedback, introspection and debugging, and
background execution and automation. We finally describe Helix, our preliminary
attempt at such a system that has already led to speedups of up to 10x on
typical iterative workflows against competing systems.

该研究描述了一种 “人机互动” 机器学习系统的愿景，它通过智能跟踪更改和中间结果，实现了工作流加速，反馈及时，内省和调试，背景执行和自动化，并描述了 Helix，初步尝试这样一个系统已经在典型的迭代工作流中实现了最多 10 倍的提速。

加速人机协同机器学习：挑战与机遇

Accelerating Human-in-the-loop Machine Learning: Challenges and  Opportunities

Recently, machine learning has been used in every possible field to leverage
its amazing power. For a long time, the net-working and distributed computing
system is the key infrastructure to provide efficient computational resource
for machine learning. Networking itself can also benefit from this promising
technology. This article focuses on the application of Machine Learning
techniques for Networking (MLN), which can not only help solve the intractable
old network questions but also stimulate new network applications. In this
article, we summarize the basic workflow to explain how to apply the machine
learning technology in the networking domain. Then we provide a selective
survey of the latest representative advances with explanations on their design
principles and benefits. These advances are divided into several network design
objectives and the detailed information of how they perform in each step of MLN
workflow is presented. Finally, we shed light on the new opportunities on
networking design and community building of this new inter-discipline. Our goal
is to provide a broad research guideline on networking with machine learning to
help and motivate researchers to develop innovative algorithms, standards and
frameworks.

本文介绍了机器学习技术在网络领域的应用及其工作流程，并针对不同的网络设计目标分析了其具体实现和优势，旨在为研究人员提供广泛的研究方向及指南。