Why choose Canonical for Enterprise AI?
- Reliable experts to speed up your AI journey
- One vendor to support your AI stack
- Run your workloads anywhere, including hybrid and multi-cloud
- Simplified operations with lifecycle management and automation
- Simple per node subscription
Develop artificial intelligence projects on any environment
Ubuntu: the OS of choice for data scientists
Develop machine learning models on Ubuntu workstations and benefit from management tooling and security patches.
Move beyond experimentation with machine learning operations (MLOps)
MLOps is the short term for machine learning operations and it stands for a set of practices that aim to simplify workflow processes and automate machine learning and deep learning deployments.
MLOps is an approach that enables you to deploy and maintain models reliably and efficiently for production, at a large scale.Learn more about MLOps
Run AI at scale with Canonical and NVIDIA
With NVIDIA AI Enterprise and NVIDIA DGX, Charmed Kubeflow improves the performance of AI workflows, by using the hardware to its maximum extent and accelerating project delivery. Charmed Kubeflow can significantly speed up model training, especially when coupled with DGX systems.
- Quick deployment
- Run the entire ML lifecycle
- Composable architectures
- Reproducibility, portability, scalability
Use modular platforms to run AI at the edge or in large clouds
Production-grade projects require a solution that enables scalability, reproducibility and portability. Canonical MLOps speeds up AI project timelines, giving you:
- The same experience on any cloud, whether private or public
- Low-ops, streamlined lifecycle management
- A modular and open source suite for reusable deployments
Open source AI services
Managed Canonical MLOps
Focus on building production grade models, while Canonical experts manage the infrastructure underneath.
- 99.9% uptime
- 24/7 monitoring
- High availability
Work with our experts to understand your data better and deliver on your use case.
- Data exploration workshop
- Canonical MLOps deployment
- MLOps workshop
Looking for Kubeflow support? Work with our team to get support for any cloud environment or CNCF-complaint Kubernetes distribution.
Open source AI resources
University of Tasmania (UTAS) modernised its space-tracking data processing with the Firmus Supercloud, built on Canonical's open infrastructure stack.
Learn how to take models to production using open source MLOps platforms.
Learn how to scale AI projects using hardware that's designed for AI workloads and certified software.
Choosing a suitable machine learning tool can often be challenging. Understand the differences between the most famous open source solutions.