# Training a Huggingface Model on AWS Sagemaker (Without Tears)

arXiv: https://arxiv.org/abs/2512.24098 · HTML: https://mlsanity.com/paper/2512.24098/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 30 Dec 2025.

## Builds on (most cited first, 3 of 3)

- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [What Language Model to Train if You Have One Million GPU Hours?](https://mlsanity.com/paper/2210.15424/) (2022)
- [What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers](https://mlsanity.com/paper/2109.04650/) (2021)

## Abstract (arXiv)

The development of Large Language Models (LLMs) has primarily been driven by resource-rich research groups and industry partners. Due to the lack of on-premise computing resources required for increasingly complex models, many researchers are turning to cloud services like AWS SageMaker to train Hugging Face models. However, the steep learning curve of cloud platforms often presents a barrier for researchers accustomed to local environments. Existing documentation frequently leaves knowledge gaps, forcing users to seek fragmented information across the web. This demo paper aims to democratize cloud adoption by centralizing the essential information required for researchers to successfully train their first Hugging Face model on AWS SageMaker from scratch.