Integrating MLOps Pipelines with DevOps for Seamless Model Deployment and Continuous Delivery
Keywords:
MLOps, DevOps, Continuous Delivery, Model Deployment, CI/CD, Version Control, AutomationAbstract
The integration of Machine Learning Operations (MLOps) with DevOps frameworks is transforming the way artificial intelligence (AI) models are deployed and maintained in production environments. By aligning MLOps pipelines with existing DevOps practices, organizations can ensure a seamless model deployment process, continuous integration, and continuous delivery (CI/CD) of AI-driven applications. This paper explores the architecture and methodology for integrating MLOps with DevOps, focusing on overcoming challenges in deployment automation, version control, and scalability. It highlights the importance of automating model retraining and monitoring, and presents case studies of successful implementations. By combining the best practices of both MLOps and DevOps, organizations can enhance their ability to deploy machine learning (ML) models in production more efficiently and with greater reliability.
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References
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