A Review of Scalable Machine Learning Architectures in Cloud Environments: Challenges and Innovations
DOI:
https://doi.org/10.32628/CSEIT25112764Keywords:
Scalable Machine Learning, Cloud Computing, Auto-ML, Serverless Computing, Microservices, Cloud Platforms, IaaS, PaaS, SaaS, Hyperparameter TuningAbstract
As the demand for machine learning (ML) and data analysis grows across industries, the need for scalable and efficient cloud-based architectures becomes critical. The increase in of data generation, along with the increasing demand for advanced analytics and machine learning (ML), has make necessary the development of scalable architectures in cloud environments. Cloud computing provides a flexible and scalable solution, allowing organizations to efficiently process large datasets and deploy complex ML models without traditional hardware limitations. The review paper explores the various cloud-based machine learning (ML) architectures, highlighting the scalability features of various cloud platforms such as AWS, Azure, and GCP. This study also discusses emerging technologies like serverless computing, automated machine learning AutoMLL), and microservices-based architectures that enhance the scalability of the cloud environment. Furthermore, challenges such as data security, talent gaps, and resource allocation inefficiencies are also considered. The paper concludes by evaluating innovative approaches that drive scalable ML in cloud environments, providing insights into the future landscape of cloud-based machine learning. In conclusion, this scalable cloud-based architecture provides a robust and flexible solution for organizations looking to implement machine learning and data analysis workflows. By leveraging distributed computing, containerization, and serverless technologies, the architecture can efficiently manage large datasets and complex models while maintaining cost-efficiency, security, and adaptability to future needs.
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