Real-Time Data Processing for Machine Learning: Streaming Architectures, Challenges, and Use Cases

• Author(s): Bhanu Prakash Reddy Rella
• Paper ID: 1707512
• Page: 230-234
• Published Date: 31-10-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 5 Issue 4 October-2021

Abstract

Real-time data processing is a critical component of modern machine learning (ML) applications, enabling rapid insights, decision-making, and automation across various industries. This paper explores the architectures, challenges, and practical use cases of real-time data streaming for ML workflows. It delves into key streaming frameworks such as Apache Kafka, Apache Flink, and Spark Streaming, highlighting their roles in efficient data ingestion, transformation, and model deployment. The challenges of real-time ML, including data latency, scalability, fault tolerance, and data quality, are analyzed with potential solutions. Furthermore, real-world applications such as fraud detection, recommendation systems, predictive maintenance, and anomaly detection are discussed to showcase the impact of real-time streaming on AI-driven systems. The paper concludes by addressing future trends in real-time ML, including edge computing, federated learning, and cloud-native streaming solutions, emphasizing their growing importance in handling dynamic and large-scale data environments.

Keywords

Real-time Data Processing, Machine Learning (ML), Data Streaming, Apache Kafka, Apache Flink.

Citations

IRE Journals:
Bhanu Prakash Reddy Rella "Real-Time Data Processing for Machine Learning: Streaming Architectures, Challenges, and Use Cases" Iconic Research And Engineering Journals Volume 5 Issue 4 2021 Page 230-234

IEEE:
Bhanu Prakash Reddy Rella "Real-Time Data Processing for Machine Learning: Streaming Architectures, Challenges, and Use Cases" Iconic Research And Engineering Journals, 5(4)