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AI consulting

Accelerate your time-to-market with Canonical's AI consulting services, designed to support you in every step of your journey.

Contact us Read our guide to MLOps

Define your AI journey.
Move projects to production faster.

Taking projects from experimentation to production requires expertise in machine learning operations. Canonical's AI consulting services support you every step of the way.

  • Explore the most suitable use cases
  • Identify improvement opportunities in the data collection process
  • Design an AI stack suitable for your organisation
  • Deploy your solution and run AI at scale
Get in touch

AI services
and pricing

Data exploration workshop

Get to know your data better and learn about possible use cases based on a data sample that you share with us.


MLOps workshop

Discover infrastructure options, MLOps processes and tools to make informed decisions about your stack.

Read the curriculum ›


Outcome-based PoC

Work with us to build a Proof of Concept (PoC) before investing in infrastructure for advanced use cases. This allows you to assess your return on investment carefully.


End-to-end consulting

Have an expert from our team by your side during the entire AI/ML project and let them drive it.

Contact us for more details ›

Fully-managed MLOps

Bring models to production quickly, while off-loading the management of your MLOps architecture.

Canonical takes care of the infrastructure while your team focuses on orchestrating success for your business.

Available on any CNCF-conformant Kubernetes, including AKS, EKS, GKE, OpenShift, Rancher and Charmed Kubernetes.

Download the datasheet ›

MLOps design and deployment

Offload the complexity of MLOps design and deployment to Canonical's engineers.

We design and deploy on any Kubernetes cluster: AKS, EKS, GKE, OpenShift, Rancher, Charmed Kubernetes, MicroK8s or other CNCF-conformant Kubernetes.

Enable portability for your AI workloads with multi-cloud and hybrid-cloud Kubeflow deployments.


Accelerate your AI journey

Straight-forward path to production for AI projects.
We follow a tried-and-true approach to help you deliver the benefits of AI at speed.

  1. Explore

    Identify the most suitable use cases where AI can bring the best return on investment.
  2. Assess

    Once you identify the use cases, select the key success metrics for your project and pinpoint potential blockers.
  3. Prepare

    Gather all the data and prepare it to start experimenting. Ingest, clean and extract the relevant features.
  4. Build

    Build an environment where it's easy to experiment and scale up down the road. Choose the most suitable architecture for your enterprise. For instance, public, private or hybrid, multi-cloud.
  5. Experiment

    Start experimenting and find the model that performs best. Automate workflows as you go to simplify your work.
  6. Deploy

    Deploy your model to production and start monitoring both your infrastructure and model.
  7. Improve

    Enhance model performance continuously and adapt it based on new datasets.

Connect with an expert to discuss your project

Contact us

AI consulting

From the smallest startups to the largest enterprises alike, organisations are using AI to make the best, fastest, most informed decisions to overcome their biggest business challenges.

Choosing a suitable machine learning tool can often be challenging. Understand the differences between the most famous open source solutions.

Learn about MLOps and get up to speed with the latest news in the industry.