Challenges and Solutions in Cross-Platform ML Systems Integration

Abstract

This paper explores the complexities of cross-platform integration within the realms of Data Engineering and Machine Learning Operations (MLOps), crucial for enhancing the lifecycle management of machine learning (ML) models. It delves into the challenges and strategies associated with integrating diverse technologies and practices to improve the scalability, reliability, and maintainability of ML systems. The paper highlights the significance of addressing technical, organizational, and cultural barriers to foster a cohesive, agile environment conducive to the iterative development and operationalization of ML models. Through a detailed examination of the multifaceted nature of these challenges, including technical interoperability, organizational alignment, and fostering a collaborative culture, the paper provides insights into navigating the complexities of integrating disparate technologies and methodologies. It also underscores the importance of strategic organizational changes, technological innovation, and a commitment to data governance and collaborative culture in achieving successful cross-platform integration. A case study of a financial firm’s integration of ML within its call center operations exemplifies the transformative potential and practical implications of effectively addressing these challenges. The paper concludes by arguing that the seamless integration of platforms and tools extends beyond technical endeavors to encompass strategic, organizational, and cultural shifts, illuminating the path toward optimizing the efficiency and effectiveness of ML model lifecycles through MLOps.