AI systems cannot exist without data. Now that AI models (data science and
AI) have matured and are readily available to apply in practice, most
organizations struggle with the data infrastructure to do so. There is a
growing need for data engineers that know how to prepare data for AI systems or
that can setup enterprise-wide data architectures for analytical projects. But
until now, the data engineering part of AI engineering has not been getting
much attention, in favor of discussing the modeling part. In this paper we aim
to change this by perform a mapping study on data engineering for AI systems,
i.e., AI data engineering. We found 25 relevant papers between January 2019 and
June 2023, explaining AI data engineering activities. We identify which life
cycle phases are covered, which technical solutions or architectures are
proposed and which lessons learned are presented. We end by an overall
discussion of the papers with implications for practitioners and researchers.
This paper creates an overview of the body of knowledge on data engineering for
AI. This overview is useful for practitioners to identify solutions and best
practices as well as for researchers to identify gaps.

AI 系统需要数据，而 AI 数据工程可以为组织提供帮助。本文通过进行数据工程的映射研究，总结了 AI 数据工程活动的论文，包括各个生命周期阶段、技术解决方案和教训，为从业者和研究人员提供了知识概览。

数据工程与 AI 系统的映射研究

What About the Data? A Mapping Study on Data Engineering for AI Systems

With the fast development of modern microscopes and bioimaging techniques, an
unprecedentedly large amount of imaging data are being generated, stored,
analyzed, and even shared through networks. The size of the data poses great
challenges for current data infrastructure. One common way to reduce the data
size is by image compression. This present study analyzes classic and deep
learning based image compression methods, and their impact on deep learning
based image processing models. Deep learning based label-free prediction models
(i.e., predicting fluorescent images from bright field images) are used as an
example application for comparison and analysis. Effective image compression
methods could help reduce the data size significantly without losing necessary
information, and therefore reduce the burden on data management infrastructure
and permit fast transmission through the network for data sharing or cloud
computing. To compress images in such a wanted way, multiple classical lossy
image compression techniques are compared to several AI-based compression
models provided by and trained with the CompressAI toolbox using python. These
different compression techniques are compared in compression ratio, multiple
image similarity measures and, most importantly, the prediction accuracy from
label-free models on compressed images. We found that AI-based compression
techniques largely outperform the classic ones and will minimally affect the
downstream label-free task in 2D cases. In the end, we hope the present study
could shed light on the potential of deep learning based image compression and
the impact of image compression on downstream deep learning based image
analysis models.

利用经典和深度学习的图像压缩方法对基于深度学习的图像处理模型进行分析并比较，表明深度学习的压缩技术在 2D 情况下在保持预测准确性的同时显著优于传统方法，并对图像压缩对下游深度学习图像分析模型的影响进行评估。