# [Train and deploy XGBoost models on Google Cloud C4A Axion VM](https://learn.arm.com/learning-paths/servers-and-cloud-computing/xgboost-on-axion/)

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/servers-and-cloud-computing/xgboost-on-axion/)
- [Understand XGBoost and Google Axion C4A for machine learning](https://learn.arm.com/learning-paths/servers-and-cloud-computing/xgboost-on-axion/background/)
- [Create Google Cloud firewall rules for XGBoost](https://learn.arm.com/learning-paths/servers-and-cloud-computing/xgboost-on-axion/firewall/)
- [Create a Google Axion C4A virtual machine for XGBoost](https://learn.arm.com/learning-paths/servers-and-cloud-computing/xgboost-on-axion/instance/)
- [Install XGBoost and train machine learning models](https://learn.arm.com/learning-paths/servers-and-cloud-computing/xgboost-on-axion/install-train-tune-xgboost/)
- [Deploy and access an XGBoost inference API](https://learn.arm.com/learning-paths/servers-and-cloud-computing/xgboost-on-axion/deploy-xgboost-inference-api/)
- [Next Steps](https://learn.arm.com/learning-paths/servers-and-cloud-computing/xgboost-on-axion/_next-steps/)

## About this Learning Path

| Skill level:                  | Introductory               |
|-------------------------------|----------------------------|
| Reading time:                 | 1 hr 30 min                |
| Last updated:                 | 31 Jul 2026                |

| Author:                       | Pareena Verma, Arm [GitHub](https://github.com/pareenaverma) [LinkedIn](https://linkedin.com/in/pareena-verma-7853607) |
|-------------------------------|----------------------------|
| Arm IP:                       | [Neoverse](https://support.arm.com/?tab=compute-ip&Product%20Type=Infrastructure%20Processors) |
| Tags:                         | [ML](https://learn.arm.com/tag/ml) [Google Cloud](https://learn.arm.com/tag/google-cloud) [Linux](https://learn.arm.com/tag/linux) [XGBoost](https://learn.arm.com/tag/xgboost) [Python](https://learn.arm.com/tag/python) [scikit-learn](https://learn.arm.com/tag/scikit-learn) [Flask](https://learn.arm.com/tag/flask) |

### Who is this for?
This is an introductory topic for DevOps engineers, ML engineers, data engineers, and software developers who want to train and deploy XGBoost machine learning models on SUSE Linux Enterprise Server (SLES) Arm64, optimize model performance, benchmark training workloads, and expose models through scalable inference APIs.

### What will you learn?
Upon completion of this Learning Path, you will be able to:
- Install and configure XGBoost on Google Cloud C4A Axion processors for Arm64
- Train and evaluate machine learning models using XGBoost
- Tune model hyperparameters and benchmark large-scale datasets
- Deploy trained XGBoost models as REST APIs and validate inference workflows

### Prerequisites
Before starting, you will need the following:
- A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
- Basic familiarity with Python and machine learning concepts