Advanced Data Engineering Techniques for Scalable AI Solutions

Authors

  • Narendra Devarasetty Anna University12, Sardar Patel Rd, Anna University, Guindy, Chennai, Tamil Nadu 600025, India Author

Keywords:

Data Engineering, Scalable AI Solutions, Data Preprocessing, Feature Engineering, Distributed Data Processing.

Abstract

As artificial intelligence (AI) continues to evolve and integrate into various industries, the 
necessity for scalable and efficient data engineering techniques becomes paramount. This paper 
explores advanced data engineering methods designed to enhance the scalability, efficiency, and 
performance of AI solutions. We investigate a range of techniques, including data preprocessing, 
feature engineering, distributed data processing, and real-time data integration, evaluating their 
impact on the performance and scalability of AI systems. Through a series of case studies and 
empirical analyses, we demonstrate how these techniques can be applied to large-scale AI 
applications to address common challenges such as data volume, velocity, and variety. Our 
findings indicate that employing these advanced methods significantly improves the ability of AI 
systems to handle complex and dynamic data environments, leading to more accurate predictions, 
faster processing times, and better overall performance. The paper concludes with 
recommendations for integrating these techniques into AI pipelines and outlines future research 
directions to further optimize data engineering practices for scalable AI solutions.

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Published

2024-09-03

How to Cite

Advanced Data Engineering Techniques for Scalable AI Solutions. (2024). International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 15(1), 272-310. http://ijmlrcai.com/index.php/Journal/article/view/67

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