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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.12916 |
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| _version_ | 1866911780239835136 |
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| author | Wu, Jiang Wang, Hongbo Ni, Chunhe Zhang, Chenwei Lu, Wenran |
| author_facet | Wu, Jiang Wang, Hongbo Ni, Chunhe Zhang, Chenwei Lu, Wenran |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12916 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models Wu, Jiang Wang, Hongbo Ni, Chunhe Zhang, Chenwei Lu, Wenran Machine Learning Artificial Intelligence 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 |
| title | Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2402.12916 |