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Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions
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AI
DOI：10.1007/978-981-96-7423-7_27.png)
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Abstract
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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.
Keywords:
Serverless Computing
Function Decomposition
Batch Processing
Scalability
Cost Efficiency
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