Implementing MLOps practices for effective machine learning model deployment: A meta synthesis

Abstract

The successful deployment of machine learning (ML) models in production environments remains a significant challenge despite advancements in ML algorithms and model development. MLOps, a set of practices that combines machine learning and DevOps principles, has emerged as a promising approach to address this challenge. This paper presents a meta-synthesis of systematic reviews to provide a comprehensive understanding of MLOps practices, tools, and challenges for effective ML model deployment. The meta-synthesis was conducted following Chrastina’s [1] methodology and included three systematic reviews and one review of MLOps products and providers. The meta-synthesis identified key MLOps principles, such as automation, reproducibility, collaboration, continuous learning, and data governance. It also revealed the main stages of the MLOps workflow, including data collection and processing, model development and training, deployment, monitoring, and retraining. Various frameworks and architectures that facilitate MLOps implementation were discussed, such as open-source plat- forms, cloud computing platforms, containerization, and container orchestration. The study highlighted the main features offered by MLOps tools, including automation, experiment tracking, versioning, monitoring, and model deployment. The most common methods of deploying ML models in production environments were identified as the use of container technologies, cloud platforms and services, and deployment of models as web services. The meta-synthesis also discussed the importance of adapted software development maturity models for assessing the maturity level of MLOps processes in organizations. Furthermore, the paper emphasized the critical roles and responsibilities involved in ML model operationalization activities, such as data scientists, data engineers, DevOps engineers, and domain experts. The main challenges encountered when deploying ML models in production environments were discussed, including managing the model lifecycle, ensuring scalability and performance, monitoring and maintaining models in real-world conditions. Open issues and challenges in MLOps were also identified, such as the need to develop standards and best practices, ensure interpretability and responsible use of models, and effectively manage data. The findings of this meta-synthesis can guide organizations in adopting MLOps practices to improve the efficiency, reliability, and scalability of their ML model deployments.