A Unified Framework for Integrating DevOps, DataOps, and MLOps Workflows
DOI:
https://doi.org/10.53469/jrse.2025.07(05).17Keywords:
Platform Ops, DevOps, DataOps, MLOps, Cloud-Native, Automation, Collaboration, Self-Service, Infrastructure as Code, Containerization, Continuous Integration, Continuous Delivery, Data Pipelines, Machine Learning, Model Deployment, Scalability, ReliabilityAbstract
The convergence of DevOps, DataOps, and MLOps is reshaping the landscape of software development and data-driven innovation. Platform Ops emerges as the unifying force, providing a robust and scalable platform that empowers these disciplines to collaborate seamlessly and deliver value faster. This paper explores the core principles and objectives of Platform Ops, highlighting its role in enhancing DevOps practices, enabling DataOps initiatives, and powering MLOps workflows. Through real-world case studies, we showcase the positive impact of Platform Ops on organizations across various industries. We also delve into emerging trends and technologies that are shaping the future of Platform Ops, emphasizing its evolving relationship with other disciplines. By embracing Platform Ops, organizations can break down silos, accelerate innovation, and achieve greater agility and efficiency in their software delivery and data management practices.
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Copyright (c) 2025 Girisha Bhat

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.