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Model Deployment, Cloud-based Machine Learning, CI/CD Pipelines, Serverless Computing, Resource Optimization.

Thus, increasing demand for the cloud-based machine learning solution is highly pushing the focus forward into making deployment pipelines for models efficient. These pipelines are very important to get a trained model to scale, provide real-time predictions, and manage the cloud infrastructure complexities in general. This paper reports on strategies improving model deployment pipelines on cloud-based ML platforms centered around automation, monitoring, and resource optimization. We investigate current tools, such as containerization, serverless computing, and CI/CD frameworks for streamlined transition pipelines through development and production. We also investigate how superior monitoring tools support the best possible resources allocation while keeping downtime at its lowest and latency low. It discusses case studies from top cloud providers and creates an optimized architecture model, especially suited to varied applications. Our experiments demonstrate that the optimized pipelines can show up to an order of magnitude improvement in terms of deployment speed, model performance, and cost effectiveness, providing a robust basis for scaling ML solutions in the cloud. Finally, we point out some of the limitations of current approaches and outline areas of future research as one considers expanding deployment pipelines in increasingly complex cloud environments.