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
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.
Discover infrastructure options, MLOps processes and tools to make informed decisions about your stack.
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.
Have an expert from our team by your side during the entire AI/ML project and let them drive it.
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.
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.
ExploreIdentify the most suitable use cases where AI can bring the best return on investment.
AssessOnce you identify the use cases, select the key success metrics for your project and pinpoint potential blockers.
PrepareGather all the data and prepare it to start experimenting. Ingest, clean and extract the relevant features.
BuildBuild 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.
ExperimentStart experimenting and find the model that performs best. Automate workflows as you go to simplify your work.
DeployDeploy your model to production and start monitoring both your infrastructure and model.
ImproveEnhance model performance continuously and adapt it based on new datasets.
Connect with an expert to discuss your projectContact us
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.