Real-Time Predictive Analytics Using Machine Learning and Cloud-Based Big Data Architectures

Abstract

Many business decisions lose value within seconds. A delayed delivery, a suspicious payment, or a failing machine calls for a prediction while there is still time to act. Traditional analytics, built on batch pipelines that process data hours after it is generated, cannot meet this need. This article proposes a cloud-based architecture for real-time predictive analytics that combines event-driven ingestion, unified stream processing, streaming and periodically retrained machine learning (ML), low-latency model serving, guardrails around automated predictions and actions, and configurable decision workflows. Using a design-oriented approach grounded in ten studies published between 2017 and 2024, the article presents the architecture and traces the lifecycle of a single event from ingestion to learning. It compares strategies for keeping models current as data changes and sets out guardrails at four levels: input, model, output, and action. A mapping shows how the reviewed evidence is distributed across the architecture. The article illustrates the architecture with an online grocery delivery platform that predicts delivery delays and intervenes before customers are affected. It argues that real-time predictive analytics depends as much on the safety and governance of automated actions as on the speed of prediction.