Overview
XGBoost 3.2.0 on Ubuntu 26.04 with Free Maintenance Support by bCloud
XGBoost 3.2.0 on Ubuntu 26.04, with maintenance support from bCloud, is a repackaged open-source offering available through the AWS Cloud Marketplace (additional charges may apply for support). XGBoost (Extreme Gradient Boosting) is a high-performance machine learning library designed for supervised learning tasks such as classification, regression, and ranking, delivering exceptional speed, scalability, and predictive accuracy.
This AWS Marketplace AMI provides a pre-configured XGBoost environment on Ubuntu 26.04 for deployment on AWS EC2, enabling data scientists, machine learning engineers, and developers to build, train, and deploy advanced predictive models with minimal setup effort.
Keywords of XGBoost
- AWS Marketplace AMI deployment
- Machine learning framework
- Gradient boosting library
- Classification and regression models
- High-performance predictive analytics
- Scalable model training
- Data science and AI workloads
- Feature importance analysis
- Optional bCloud maintenance support
Core Technical Capabilities of XGBoost
Gradient Boosting Framework
XGBoost implements optimized gradient boosting algorithms for accurate predictive modeling.
- supports classification, regression, and ranking tasks
- advanced tree boosting algorithms
- high predictive performance across diverse datasets
High Performance and Scalability
XGBoost is designed for efficient training and inference on datasets of varying sizes.
- parallel and distributed processing support
- optimized memory utilization
- fast model training and evaluation
Feature Engineering and Analysis
XGBoost provides tools that help improve model interpretability and performance.
- feature importance scoring
- handling of missing values
- built-in regularization to reduce overfitting
Integration with Data Science Ecosystems
XGBoost integrates seamlessly with popular machine learning and analytics tools.
- compatible with Python, R, Java, and Scala
- works with NumPy, Pandas, and Scikit-learn
- supports modern machine learning workflows
Production-Ready Machine Learning
XGBoost is widely used in enterprise and research environments for deploying predictive models.
- reliable model deployment capabilities
- supports large-scale analytical workloads
- suitable for cloud and on-premises environments
AWS Marketplace-Optimised Advantages
AMI-Based EC2 Deployment
AWS Marketplace AMI deployment provides:
- XGBoost 3.2.0 pre-installed on Ubuntu 26.04
- ready-to-use machine learning environment
- reduced setup and configuration time
AWS Infrastructure Compatibility
XGBoost environments on EC2 can be managed using standard AWS tools:
- VPC and Security Groups for secure access
- EBS for scalable storage and datasets
- integration with monitoring and logging tools
Procurement and Billing
AWS Marketplace supports:
- centralised billing through AWS account
- simplified procurement and deployment
Maintenance Support (bCloud)
Optional bCloud support may include:
- updates and patch management
- technical troubleshooting
- deployment and operational assistance
Support beyond the open-source XGBoost environment may incur additional charges.
Highlights
- Supports classification, regression, and ranking tasks.
- Automatic handling of missing data values.
- Integrates with NumPy, Pandas, and Scikit-learn.
Details
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Features and programs
Financing for AWS Marketplace purchases
Pricing
Dimension | Cost/hour |
|---|---|
m4.large Recommended | $0.03 |
t2.micro | $0.01 |
t3.micro | $0.03 |
m3.medium | $0.03 |
c3.large | $0.03 |
c4.large | $0.03 |
c5.large | $0.03 |
t3.small | $0.03 |
m5.large | $0.03 |
t2.small | $0.03 |
Vendor refund policy
No Refund
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Packaged with latest updates as of May 2026.
Additional details
Usage instructions
Connect your instance via SSH, the username is ubuntu. More info on SSH: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/AccessingInstancesLinux.html - Run the following commands: #sudo su #apt update #cd /opt/xgboost #source venv/bin/activate #python -c "import xgboost; print(xgboost.version)"
Support
Vendor support
Feel free to reach out anytime. Our support team is available 24x7 for assistance. Phone: +1 (408) 646-8523 Email: cloud@bcloud.ai Website:
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