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

翻译：数据管道在机器学习建模和数据产品开发等任务中扮演着不可或缺的角色。随着数据来源的日益多样化和复杂化，以及数据量的快速增长，构建高效的数据管道已成为提升工作效率和解决复杂问题的关键。本文重点探讨如何通过集成自动机器学习（AutoML）与数据管道，以自动化机器学习方法优化数据流。我们将讨论如何利用AutoML技术提升数据管道的智能化水平，从而在机器学习任务中取得更优成果。通过深入探究数据流的自动化与优化，我们揭示了构建能适应不断变化数据环境的高效数据管道的关键策略。这不仅加速了建模过程，还为复杂问题提供了创新解决方案，使得在日益复杂的数据领域中能取得更显著的效果。关键词—数据管道训练；自动机器学习；数据环境；机器学习