Vol. 3 No. 2 (2023): African Journal of Artificial Intelligence and Sustainable Development
Articles

Incorporating Automated Machine Learning and Neural Architecture Searches to Build a Better Enterprise Search Engine

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  • Sarbaree Mishra
Sarbaree Mishra
Program Manager at Molina Healthcare Inc., USA
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Published 13-12-2023

Keywords

  • Enterprise Search Engine,
  • Automated Machine Learning
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Sarbaree Mishra, “Incorporating Automated Machine Learning and Neural Architecture Searches to Build a Better Enterprise Search Engine”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 2, pp. 507–527, Dec. 2023, Accessed: Dec. 18, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/219

Abstract

The field of enterprise search engines has long grappled with the challenge of delivering search results that are both highly relevant and efficient, especially as data volumes and user expectations continue to grow. Traditional search engine models often need to provide the level of personalization, accuracy, and speed that modern businesses demand. With the rapid advancements in Artificial Intelligence (AI), a new frontier has emerged in the form of Automated Machine Learning (AutoML) and Neural Architecture Search (NAS). These innovative technologies offer the potential to transform the way search engines are designed and optimized. AutoML simplifies the model selection and training process by automatically identifying the best algorithms for a given task. At the same time, NAS focuses on the automated search for the most effective neural network architectures. This combination can lead to more efficient search systems by enhancing the ability to tailor results to the specific needs of users and businesses. AutoML allows search engines to adapt quickly to changing data patterns and user behaviours, while NAS optimizes the underlying neural networks for better performance. This fusion of technologies offers a pathway to more scalable, efficient, and dynamic enterprise search solutions. Organizations can focus on fine-tuning their search strategies, improving user experience, and leveraging data by automating the often complex and time-consuming tasks of model and architecture selection. While integrating AutoML and NAS into enterprise search engines presents significant opportunities, it also introduces challenges, particularly in ensuring the systems remain interpretable and transparent for end users. Nonetheless, these challenges can be mitigated with careful design and thoughtful implementation. As businesses grapple with the ever-growing amounts of unstructured data, AI-driven search engines will only become more critical, promising to unlock new levels of performance and relevance in enterprise search. This approach not only enhances the quality of search results but also improves operational efficiencies, paving the way for more intelligent, user-centric search experiences that adapt to the needs of each user.

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