Year: 2024
Type: article
Abstract: In the rapidly evolving landscape of generative artificial intelligence, the fundamental infrastructure of data pipelines emerges as a critical determinant of model performance and efficacy.This comprehensive article explores the architectural considerations, methodological approaches, and emerging trends in data pipeline design for AI systems.The article examines how modern pipeline architectures have evolved from basic ETL processes to sophisticated neural highways, incorporating advanced preprocessing methodologies, scalable infrastructure, and seamless machine learning workflow integration.Through analysis of enterprise implementations across various sectors, this study demonstrates how optimized pipeline architectures Hari
Author: Hari Kiran Vuyyuru
Institution: Texas A&M University
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4405855007