> For the complete documentation index, see [llms.txt](https://federico-trotta.gitbook.io/federico-trottas-portfolio/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://federico-trotta.gitbook.io/federico-trottas-portfolio/my-portfolio/api-documentation/beam-api-training-a-machine-learning-model-on-a-serverless-gpu.md).

# Beam API: Training a Machine Learning Model on a Serverless GPU

[Beam](https://www.beam.cloud/) is a service that provides Data Scientists and Developers the possibility to run code on a serverless GPU, utilizing their REST API.

I had the possibility to collaborate with them to [their documentation](https://docs.beam.cloud/getting-started/introduction), creating a use case that describes:

* What are serverless GPUs and why they're revolutionizing the world of data in terms of development.
* How to log in and install the beam CLI on a Linux machine (or, via WSL in Windows).
* A use case on how to train a Machine Learning model on the Beam serverless GPU, using its API.

Read [the complete use case here](https://levelup.gitconnected.com/accelerating-ai-how-serverless-gpus-are-revolutionizing-model-training-af14dd978d64).