Etl Pipeline Integration for Machine Learning-based Product Classification: a Comprehensive Guide
Year: 2025
Type: article
Abstract: This article explores the integration of machine learning-based product classification systems within modern ETL (Extract, Transform, Load) pipelines.The article examines how organizations are revolutionizing their e-commerce platforms through automated classification systems, demonstrating significant improvements in operational efficiency and accuracy.The article analyzes the technological infrastructure supporting these systems, including distributed computing architectures, real-time processing capabilities, and advanced optimization techniques.Through extensive analysis of enterprise implementations, the article documents the transformation in product categorization methodologies, from traditional manual approaches to sophisticated AI-driven solutions.The article highlights the critical role of proper pipeline integration in achieving optimal performance and reliability, while also addressing key considerations in scalability, monitoring, and maintenance.The article provides valuable insights into best practices for implementing ML-based classification systems, emphasizing the importance of sustainable computing practices and efficient resource utilization in modern e-commerce environments.
Authors: Anurag Awasthi, Aniket Vaidya
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408249111