Real-time Machine Learning: Algorithms and Applications in Stream Processing

Abstract

Real-time machine learning (RTML) has gained increasing importance in various domains, enabling timely insights and decision-making from streaming data. This paper provides an overview of RTML, discussing algorithms, techniques, and applications in stream processing environments. We explore key challenges in deploying machine learning models in real-time settings and review state-of-the-art approaches to address these challenges. Additionally, we present case studies illustrating the practical applications of RTML in diverse fields such as finance, healthcare, and Internet of Things (IoT). The key innovation of RTML lies in its ability to handle the velocity, volume, and variety of streaming data, while also accommodating constraints such as limited memory and processing resources. By leveraging techniques such as online learning, incremental updating, and adaptive model selection, RTML algorithms can adapt to evolving data distributions and make informed decisions in dynamic environments. Moreover, RTML finds applications in a wide range of domains, including finance, healthcare, telecommunications, Internet of Things (IoT), and cybersecurity. In finance, for example, RTML algorithms are used for high-frequency trading, fraud detection, and risk management, where timely insights can have a significant impact on business outcomes.