Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913693980164096 |
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| author | Barrak, Amine Ksontini, Emna |
| author_facet | Barrak, Amine Ksontini, Emna |
| contents | As data-intensive applications grow, batch processing in limited-resource environments faces scalability and resource management challenges. Serverless computing offers a flexible alternative, enabling dynamic resource allocation and automatic scaling. This paper explores how serverless architectures can make large-scale ML inference tasks faster and cost-effective by decomposing monolithic processes into parallel functions. Through a case study on sentiment analysis using the DistilBERT model and the IMDb dataset, we demonstrate that serverless parallel processing can reduce execution time by over 95% compared to monolithic approaches, at the same cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12017 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions Barrak, Amine Ksontini, Emna Distributed, Parallel, and Cluster Computing Machine Learning As data-intensive applications grow, batch processing in limited-resource environments faces scalability and resource management challenges. Serverless computing offers a flexible alternative, enabling dynamic resource allocation and automatic scaling. This paper explores how serverless architectures can make large-scale ML inference tasks faster and cost-effective by decomposing monolithic processes into parallel functions. Through a case study on sentiment analysis using the DistilBERT model and the IMDb dataset, we demonstrate that serverless parallel processing can reduce execution time by over 95% compared to monolithic approaches, at the same cost. |
| title | Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2502.12017 |