International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
A REVIEW OF ADAPTIVE MODEL RETRAINING TRIGGER MECHANISM USING CONCEPT DRIFT QUANTIFICATION IN STREAMING CLOUD DATA PIPELINES Abhay Singh1, Mrs. Arifa Khan2 1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India 2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology,
Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The rapid proliferation of streaming data in
drift quantification and outlines the objectives and scope of this review.
cloud-centric environments has intensified the need for robust machine learning (ML) models capable of adapting to nonstationary data distributions. In dynamic data streams, concept drift—defined as changes in the statistical properties of the target variable or feature space over time—can significantly degrade model performance. Traditional static retraining schedules are often inefficient, leading either to unnecessary computational overhead or delayed adaptation. Consequently, adaptive model retraining trigger mechanisms driven by concept drift quantification have emerged as a critical research area.
1.1 Background 1.1.1 Streaming Data Systems and Cloud Integration Streaming data systems are designed to process continuous, unbounded data flows with low latency and high throughput. Distributed frameworks such as Apache Kafka, Apache Spark, and Apache Flink enable scalable event-driven architectures within cloud environments. These systems leverage elastic resource provisioning, containerization, and micro services to handle fluctuating workloads efficiently (Kreps et al., 2011; Zaharia et al., 2016).
This review systematically examines existing approaches for detecting and quantifying concept drift within streaming cloud data pipelines and analyzes how these quantification strategies inform adaptive retraining decisions. The study categorizes drift detection techniques into statistical, windowbased, distribution-based, and ensemble-driven methods, and evaluates their applicability in cloud-native streaming architectures. Furthermore, it synthesizes retraining trigger mechanisms, including threshold-based, performance-driven, and hybrid frameworks, highlighting their computational trade-offs and scalability considerations. By identifying methodological trends, practical deployment challenges, and research gaps, this review provides a structured understanding of adaptive retraining strategies and outlines future research directions for resilient, cost-aware, and scalable ML systems in real-time cloud environments.
Cloud integration enhances fault tolerance, horizontal scalability, and cost optimization by decoupling storage and computation layers. Server less and managed streaming services further reduce operational overhead while supporting continuous ML inference pipelines. As organizations increasingly rely on real-time analytics for fraud detection, recommendation engines, and IoT monitoring, streaming ML models have become integral to cloud-native architectures (Carbone et al., 2015). 1.1.2 Importance of Machine Learning Models in RealTime Decision Systems Real-time decision systems depend on ML models capable of producing rapid and accurate predictions from streaming inputs. Applications such as credit risk scoring, predictive maintenance, cyber security threat detection, and dynamic pricing require low-latency inference pipelines. Unlike batch-learning environments, streaming contexts demand continuous adaptation to evolving data distributions.
Key Words: Concept Drift; Adaptive Retraining; Streaming Data Pipelines; Drift Quantification; Cloud Computing; Online Machine Learning
1. INTRODUCTION The proliferation of large-scale, high-velocity data streams has fundamentally transformed how machine learning (ML) models are deployed and maintained. Modern cloud-native infrastructures enable real-time ingestion, processing, and analysis of streaming data across distributed environments. However, the non-stationary nature of streaming data introduces significant challenges to maintaining predictive reliability. This section contextualizes the emergence of adaptive retraining trigger mechanisms driven by concept
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Online and incremental learning algorithms enable models to update parameters progressively as new data arrives (Gama et al., 2014). However, when deployed in production, many ML systems rely on static models retrained periodically, which may lead to performance degradation under distributional shifts. Maintaining model validity in dynamic environments therefore requires systematic monitoring and adaptive retraining strategies.
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