• Rahul Roy Devarakonda

Scalability, automation, and effective lifecycle management are some of the major obstacles to integrating machine learning (ML) models into commercial settings. Conventional machine learning pipelines are not appropriate for contemporary corporate applications because to their fragmentation, human intervention, and deployment inefficiencies. MLOps, a combination of DevOps with machine learning techniques, has arisen to tackle these problems by enabling real-time model monitoring, continuous integration, and continuous deployment. Using containerization, automation tools, and AI-driven monitoring frameworks, this article suggests a modular and reusable architecture for smooth ML model integration into a DevOps pipeline. To improve model portability and scalability, the suggested framework integrates multi-cloud orchestration techniques, agile deployment models, and software-defined networking (SDN) concepts. Furthermore, it incorporates design principles for ML model deployment and data interoperability techniques to guarantee effective version control and automated rollback processes. This design greatly shortens deployment time, increases operating efficiency, and improves overall model dependability by allocating computational resources optimally and utilizing real-time drift detection approaches. According to experimental assessments, this method can improve resource consumption by 30%, decrease ML deployment times by up to 70%, and increase scalability in cloud-native contexts. According to the results, a well-planned, modular MLOps architecture guarantees high-performance, scalable, and sustainable machine learning operations in addition to improving automation and reusability. By laying a strong basis for enterprise-scale ML integration, this study advances the development of next-generation AI-driven DevOps frameworks.

MLOps Contigious integration, Deployment (CI/CD), Machine learning DevOps Automated, Implementation of ML Models, Containerization and Microservices, Model Monitoring and Sfeature Storage, Modular and Scalable Machine learning Architecture.

"A Modular and Reusable Architecture for Integrating Machine Learning Models in a DevOps Pipeline", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 9, page no.f589-f596, September 2022, Available :http://www.jetir.org/papers/JETIR2209601.pdf

"A Modular and Reusable Architecture for Integrating Machine Learning Models in a DevOps Pipeline", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 9, page no. ppf589-f596, September 2022, Available at : http://www.jetir.org/papers/JETIR2209601.pdf