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arXiv · 2502.12017

Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions

Amine Barrak·Emna Ksontini
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Abstract

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.

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BibTeXRIS

Amine Barrak, Emna Ksontini. 2025-01-30. Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions. https://arxiv.org/abs/2502.12017

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