DevOps and MLOps: Integrating CI/CD Pipelines for Scalable AI Model Deployment
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P101Keywords:
Continuous Integration (CI), Continuous Deployment (CD), Machine Learning Operations (MLOps), Model Versioning, Automated Testing, Containerization (Docker, Kubernetes), Model Monitoring and Drift Detection, Infrastructure as Code (IaC), Scalable Deployment, Pipeline Orchestration (Airflow, MLflow)Abstract
The integration of DevOps and MLOps practices has become increasingly important in the modern software development and machine learning (ML) landscape. As organizations strive to deploy and manage AI models at scale, the need for robust, automated, and continuous integration and deployment (CI/CD) pipelines has become paramount. This paper explores the synergies between DevOps and MLOps, focusing on how the integration of these practices can enhance the scalability, reliability, and efficiency of AI model deployment. We discuss the challenges and solutions associated with this integration, including the use of version control, automated testing, and monitoring. We also present a case study and a detailed algorithm for implementing a CI/CD pipeline that supports both software development and ML model deployment. The paper concludes with a discussion of future trends and the potential impact of this integration on the broader technology ecosystem
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References
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