Operationalizing MLOps: A Comparative Case Study of CI/CD Pipeline Implementations for AI and Machine Learning
Keywords:
MLOps, CI/CD (Continuous Integration/Continuous Deployment), Machine Learning, Artificial Intelligence, DevOps, Case Study, Pipeline AutomationAbstract
Background: The transition of machine learning (ML) and artificial intelligence (AI) models from research to production has exposed significant operational challenges. While Continuous Integration/Continuous Deployment (CI/CD) is a mature practice in traditional software engineering, its application in the ML lifecycle (MLOps) presents unique complexities, including data versioning, model retraining, and continuous monitoring. There is a notable gap in the literature regarding comprehensive case studies of successful, end-to-end CI/CD implementations for ML [31,32].
Objectives: This paper aims to identify the architectural patterns, key success factors, and best practices of successful CI/CD pipeline implementations for ML and AI systems through a comparative analysis of real-world case studies.
Methods: A qualitative, multiple case study methodology was employed. Data was systematically collected from publicly available, detailed accounts of CI/CD implementations from diverse industries, including e-commerce, healthcare, and finance. A thematic analysis framework was used to extract and compare key aspects such as pipeline architecture, toolchains, automation strategies, and measured outcomes.
Results: The analysis of the case studies revealed several common success patterns, including the extensive use of containerization, the adoption of centralized feature stores for managing ML-specific data, and the implementation of robust automated testing and validation stages beyond traditional code checks. Key differences were observed in pipeline design based on industry-specific requirements, such as regulatory compliance in healthcare and real-time inference demands in finance. Each case demonstrated a significant, measurable improvement in deployment velocity, operational stability, and model performance [34].
Conclusion: A well-architected CI/CD pipeline is a critical enabler for scaling ML and AI initiatives effectively. The findings from these case studies provide a practical framework and actionable insights for organizations seeking to build and refine their MLOps capabilities, moving from ad-hoc model deployment to a systematic, automated, and reliable process.
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Copyright (c) 2025 Michael Davis, Prof. Evelyn Reed
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