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

arXiv: https://arxiv.org/abs/2502.12017 · HTML: https://mlsanity.com/paper/2502.12017/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 30 Jan 2025.

## Builds on (most cited first, 6 of 6)

- [Towards Demystifying Serverless Machine Learning Training](https://mlsanity.com/paper/2105.07806/) (2021)
- [Cloud-Native Computing: A Survey from the Perspective of Services](https://mlsanity.com/paper/2306.14402/) (2023)
- [A Holistic View on Resource Management in Serverless Computing Environments: Taxonomy and Future Directions](https://mlsanity.com/paper/2105.11592/) (2021)
- [Exploring the Impact of Serverless Computing on Peer To Peer Training Machine Learning](https://mlsanity.com/paper/2309.14139/) (2023)
- [SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless ML Training Architecture](https://mlsanity.com/paper/2309.14148/) (2023)
- [Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud](https://mlsanity.com/paper/2303.11088/) (2023)

## Abstract (arXiv)

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.