AI / ML from workstation to production

Ubuntu is the platform to power your Artificial Intelligence ambitions — from developer workstations, to racks, to clouds and to the edge with smart connected IoT.

Canonical’s Kubeflow supports the most popular tools for machine learning — starting with JupyterHub and Tensorflow — in a standardised workflow running on Kubernetes.

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Develop your AI Models on Ubuntu

Develop AI models on high-end Ubuntu workstations. Train on racks of bare metal Kubernetes or public clouds with hardware acceleration. Deploy to edge and IoT. All on Ubuntu, delivered by Canonical.

Kubeflow — AI on Kubernetes — anywhere

Google and Canonical collaborate on Kubeflow, a standardised machine learning solution for on-premise and on-cloud training. Leveraging Ubuntu, you benefit from perfect multi-cloud portability of AI/ML workloads.

Workstation AI

Ubuntu-certified workstations from Dell and HP with NVIDIA, microk8s and Kubeflow

  • Accelerate data science
  • Lightest footprint
  • Laptop to workstation
  • GPGPU optional
  • Develop and test AI

Bare metal AI

Kubernetes on bare metal with NVIDIA GPGPU acceleration

  • Highest performance
  • On-premise with local data
  • Hardware recommendations
  • Fully managed options

Google Cloud AI

GKE on Ubuntu with NVIDIA GPGPU acceleration

  • Effectively infinite scale
  • Portable workloads
  • Fastest cloud ML

Canonical Cloud AI

Kubeflow on Kubernetes on Openstack with NVIDIA GPGPU acceleration

  • Maximize benefits of OpenStack
  • On-premise with local data
  • Hardware recommendations
  • Fully managed options

Kubeflow features

Kubeflow brings together all the most popular tools for machine learning, starting with JupyterHub and Tensorflow, in a standardised workflow running on Kubernetes. Optimised on a wide range of hardware and cloud infrastructure, Kubeflow lets your data scientists focus on the pieces that matter to the business.

It is an extensible framework, which allows you to leverage the tools of your choice. Start with Tensorflow and JupyterHub or bring your own frameworks and tools. Combined with Kubeflow’s automation, this will accelerate your machine learning activities — from model development to model training to model sharing.

Initiated by Google on Ubuntu for perfect portability of AI workloads from your workstation, to your data center rack on Canonical’s bare metal k8s or Canonical’s OpenStack virtualization, to Google’s Cloud Kubernetes service GKE which also runs on Ubuntu. Simple.

Canonical’s Kubeflow and Kubernetes on bare metal servers, with NVIDIA GPGPUs, provides an ultra high-performance machine learning cluster. Deployment, support, and optional remote management and remote operations make it the best way to accelerate your data science and machine learning.

Canonical has provided both a familiar and highly performant operating system that works everywhere. Whether on-premise or in the cloud, software engineers and data scientists can use tools they are already familiar with, such as Ubuntu, Kubernetes and Kubeflow, and greatly accelerate their ability to deliver value for their customers.

David Aronchick, Google Product Manager for Kubeflow

in partnership with

Consulting to get started, Managed Ops to keep you focused

Turn on the taps with a workshop to understand the full stack of machine learning. Build a full pipeline from developer stations to your data center, to the public cloud. Canonical works with the leading companies to ensure you have the widest range of choices. First, start with one of our standard bare metal Kubernetes service packages (Discoverer or Discoverer Plus) and then select the AI Add-on to unlock the benefits of AI on Kubernetes.

Learn more about our Kubernetes packages ›

AI add-on for Kubernetes Discoverer and Discoverer Plus

AI/ML Add-on

Specialised AI/ML workshop and readiness assessment

One additional workshop day dedicated to Kubeflow, including Tensorflow and JupyterHub, covering everything your business needs for on-prem/off-prem AI/ML operations.

  • On site or remote options
  • Hands-on K8s and Kubeflow
  • Full pipeline view

Canonical and a data science partner will deliver an AI assessment as part of the workshop to help you determine the readiness of your existing data and analytics for ML and AI approaches.

  • Understand AI lifecycle
  • Preliminary data and process discovery
  • Development capacity assessment
  • Deploy and operate ML analysis
  • Finalize initial AI strategy

Contact us ›

IoT and Edge AI

Train in the cloud. Act at the edge.

Cameras, music systems, cars, even firewalls and CPE are becoming smarter. From natural language processing to image recognition, from real-time high-speed autonomous navigation to network intrusion detection. Ubuntu gives you a seamless operational framework for development, training and inference all the way out to the edge.

Leaders in artificial intelligence choose Ubuntu

  • Amazon
  • Google
  • Microsoft
  • Tesla
  • OpenAI
  • IBM
  • Facebook

Organizations are increasingly looking to accelerate their deep learning and AI implementations. In addition to using Ubuntu on our DGX systems, we have been working with Canonical to offer Kubernetes on NVIDIA GPUs as a scalable and portable solution for multi-cloud deep learning training and inference workloads.

Duncan Poole, Director of Platform Alliances at NVIDIA

Partner with us

It takes an open ecosystem to solve the diverse challenges of AI infrastructure across every sector and in every region. Our partners ensure that you have the widest range of capabilities available for automated integration in your cloud, and that you can get insight and support locally.

To learn more about our partners or becoming a Canonical AI partner, please contact us today.

Get in touch or learn more about partnering with us

Get started with AI and Machine learning today.

Test drive Kubeflow now

Or contact our experts to get started with consulting, training or outsourced operations.