An enterprise guide to private AI infrastructure
How to navigate the complexity of building your private cloud for AI with open source
About the guide
Managing the complexity of private AI infrastructure can be a challenging proposition. It requires a tightly integrated ecosystem spanning specialized silicon, networking, container orchestration, data, MLOps software, and more.
Open source software provides a transparent and flexible foundation for this architecture – particularly given the critical role of Kubernetes in AI workloads and the prevalence of open source software across the AI landscape. As such, using a unified, end-to-end open source stack represents the most logical path to enterprise-grade AI infrastructure.
Building AI infrastructure on Ubuntu enables organizations to take advantage of Canonical’s extensive partner ecosystem – which includes industry leaders such as NVIDIA, Intel, and AMD – to solve the notorious hardware enablement problem and freely choose the optimal silicon for their use case.
Download the guide to:
- explore different layers of AI infrastructure,
- examine the benefits and applications of public, private and hybrid clouds for AI workloads,
- and get the blueprint to build a private cloud for AI using Canonical’s open source stack.
Read the guide to learn more about open source full-stack AI infrastructure.
