AWS Step Functions for Serverless Deep Learning Model Training Pipelines

Dr. Javier Morales

Associate Professor, Department of Computer Engineering and Cloud Systems, University of Madrid, Spain

DOI: 10.63665/ijmlaidse-y1f1a005

Abstract

This work may look into the future at how to make the functionality work on more than one cloud. That will free organizations from vendor lock-in and allow them to take advantage of price differences between Google Cloud, AWS, and Microsoft Azure. If schedulers using reinforcement learning are included, the system could automatically choose and allocate Spot Instances depending on system load and the probability of instance interruptions. In addition, developing training algorithms that tolerate interruptions and reduce checkpointing overhead can further improve efficiency. The overall machine learning workflow can also be enhanced if automated pipelines support other AutoML tasks such as model deployment, monitoring, and continuous data integration. Such integrated automation can significantly improve resource utilization while maintaining cost-efficient machine learning operations in large-scale cloud infrastructures.

Keywords

Serverless Computing, AWS Step Functions, Deep Learning Pipelines, Machine Learning Operations (MLOps), Model Training Automation, Cloud Orchestration, AWS Lambda, SageMaker, Event-Driven Architecture, Cost-Efficient ML Workflows

1. Introduction

Payroll management is a critical organizational function that involves calculating employee compensation, bonuses, deductions, and benefits. As organizations grow in scale and complexity, payroll systems become increasingly vulnerable to fraudulent activities such as ghost employees, inflated work hours, manipulated compensation structures, and unauthorized financial adjustments. Conventional fraud detection systems rely heavily on static rules and manual audits, which are insufficient in identifying complex and evolving fraud schemes.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies introduces adaptive mechanisms capable of learning patterns from historical payroll data and identifying anomalies in real time. These intelligent systems enhance transparency, minimize human bias, and reduce financial losses. By leveraging predictive analytics, classification models, clustering techniques, and neural networks, organizations can implement proactive detection strategies rather than reactive correction mechanisms.

This study aims to present an in-depth overview of AI-based advanced models for payroll fraud detection, evaluating their methodologies, implementation frameworks, and practical benefits within enterprise environments.

References