Machine Learning in the Cloud: Best Practices and Use Cases
DOI:
https://doi.org/10.15680/IJCTECE.2024.0702001Keywords:
Machine Learning in the Cloud, Cloud-based Machine Learning, ML in Cloud Computing, Cloud ML services, Cloud ML platforms, ML model deployment, Cloud infrastructure for ML, ML lifecycle management, Scalable ML pipelines, AWS SageMaker, Google Cloud AI PlatformAbstract
The advent of cloud computing has revolutionized how machine learning (ML) models are developed, trained, and deployed. By providing scalable, on-demand infrastructure, cloud platforms empower researchers, startups, and enterprises to leverage advanced ML capabilities without the burden of maintaining expensive hardware. This paper explores best practices and diverse use cases for implementing machine learning in the cloud, focusing on resource optimization, workflow automation, and model lifecycle management.
Cloud-based machine learning offers several strategic benefits including cost-efficiency, ease of access to high- performance computing (HPC), and the integration of managed services for data preprocessing, model training, deployment, and monitoring. Popular services such as Amazon SageMaker, Google Cloud Vertex AI, and Microsoft Azure ML provide comprehensive environments that simplify end-to-end ML development. However, challenges such as data privacy, vendor lock-in, and cost unpredictability remain persistent concerns.
This paper reviews recent literature and analyzes real-world applications of cloud ML in industries like healthcare, finance, and retail. It outlines a research methodology centered around evaluating ML workflows across leading cloud platforms, followed by a discussion of key findings on performance, cost, and scalability. A structured workflow is proposed to guide practitioners in selecting appropriate tools and architectures.
Furthermore, the paper identifies the primary advantages and drawbacks of cloud-based ML, concluding with recommendations for overcoming current limitations and future research directions. Use cases such as fraud detection, medical diagnostics, customer segmentation, and predictive maintenance demonstrate the transformative potential of cloud-based ML when implemented with best practices.
By highlighting successful implementations and addressing operational trade-offs, this work serves as a practical guide for decision-makers and ML practitioners aiming to maximize the value of machine learning in the cloud. Through a comprehensive examination of tools, techniques, and real-world scenarios, it aims to contribute to more efficient, ethical, and scalable ML solutions.
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