# Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models

arXiv: https://arxiv.org/abs/2402.12916 · HTML: https://mlsanity.com/paper/2402.12916/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms](https://mlsanity.com/paper/2312.12872/) (2023)

## Built on by (most cited first, 5 of 5)

- [Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems](https://mlsanity.com/paper/2404.00903/) (2024)
- [Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning](https://mlsanity.com/paper/2403.19345/) (2024)
- [Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis](https://mlsanity.com/paper/2403.17549/) (2024)
- [RNA Secondary Structure Prediction Using Transformer-Based Deep Learning Models](https://mlsanity.com/paper/2405.06655/) (2024)
- [Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance](https://mlsanity.com/paper/2506.23314/) (2025)

## Abstract (arXiv)

Data Pipeline plays an indispensable role in tasks such as modeling machine learning and developing data products. With the increasing diversification and complexity of Data sources, as well as the rapid growth of data volumes, building an efficient Data Pipeline has become crucial for improving work efficiency and solving complex problems. This paper focuses on exploring how to optimize data flow through automated machine learning methods by integrating AutoML with Data Pipeline. We will discuss how to leverage AutoML technology to enhance the intelligence of Data Pipeline, thereby achieving better results in machine learning tasks. By delving into the automation and optimization of Data flows, we uncover key strategies for constructing efficient data pipelines that can adapt to the ever-changing data landscape. This not only accelerates the modeling process but also provides innovative solutions to complex problems, enabling more significant outcomes in increasingly intricate data domains. Keywords- Data Pipeline Training;AutoML; Data environment; Machine learning