Optimizing Computational Efficiency in Machine Learning Workflows Using Cloud-Based Infrastructure

George Davies Harrison

Authors

George Davies Harrison Research Associate - Machine Learning and AI, United Kingdom Author

Keywords:

Machine Learning, Cloud Computing, Computational Efficiency, Infrastructure Optimization, Auto-scaling, TPUs, Elastic Compute Resources

Abstract

As machine learning (ML) applications scale in complexity and data volume, optimizing computational efficiency becomes crucial. This research investigates strategies for enhancing computational efficiency in ML workflows by leveraging cloud-based infrastructure. Utilizing elastic compute resources, parallel processing, and optimized data storage, cloud-based platforms enable significant reductions in training times and cost-efficiency improvements. Through an analysis of existing literature, this paper examines how cloud providers’ advanced features, such as serverless computing, auto-scaling, and specialized hardware (e.g., TPUs), contribute to the efficient deployment of ML workflows. Performance metrics and comparative analyses demonstrate the practical benefits of cloud optimization in ML, paving the way for scalable, resource-efficient model deployment.

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