MLOps-Based Cloud Deployment Pipeline for Scalable ML Workloads

Abstract: Machine learning models are increasingly used across industries, but their effectiveness depends not only on accuracy during development but also on efficient deployment for real-world applications. Traditional through distributed resources and managed services. The work “Cloud-Based Machine Learning Model Deployment” focuses on building a framework to deploy, manage, and scale machine learning models seamlessly using cloud platforms such as AWS, Azure, or Google Cloud. It ensures high availability, low latency, and easy integration with client applications through REST APIs and microservices. Existing systems often deploy ML models manually on local servers, which leads to difficulties in version control, limited scalability, higher maintenance costs, and inefficiency in handling large-scale data requests. The proposed system leverages containerization tools like Docker and orchestration platforms like Kubernetes to automate deployment, enable auto-scaling, and ensure secure model serving across multiple environments. Algorithms such as Random Forest, Support Vector Machines, and Neural Networks can be trained offline and deployed in the cloud, while additional techniques like CI/CD pipelines and model monitoring ensure real-time performance optimization and reliability. This approach enhances scalability, efficiency, and accessibility for enterprises adopting ML-driven solutions.