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Canonical MLOps
London, United Kingdom
What it is
Canonical MLOps is a free, open-source platform for moving machine learning models from experimentation into production across the ML lifecycle. Its stack combines Charmed Kubeflow, Charmed MLflow, and Charmed Feast. Kubeflow deploys, scales, and manages AI workflows across clouds, virtual machines, or bare metal. MLflow provides experiment tracking, reproducible projects, a model registry, and model deployment, while Feast supplies a centralized feature registry, offline and online stores, real-time and batch ingestion, and pipeline versioning. The platform includes automated workflows, security patching, and integrations with tools such as Jupyter, Grafana, and Prometheus. MLflow can run on a workstation, CNCF-conformant Kubernetes, or a public cloud. Kubeflow supports AWS, Azure, and GCP Kubernetes services, including EKS, AKS, and GKE. Canonical says components receive up to 15 years of security maintenance, regular updates, and CVE fixes.
For and against
For
- Open-source stack with a free plan
- Includes Kubeflow, MLflow, and Feast
- Supports experiment tracking and model registries
- Runs across clouds, Kubernetes, and bare metal
- Up to 15 years of security maintenance
Against
- Optional support is billed per node, per year
- Deployment requires technical infrastructure
The verdict
Canonical MLOps brings workflow orchestration, tracking, registries, serving, and feature management into one open-source stack. Its broad deployment options and long security-maintenance statement are useful for teams planning sustained operations.
Who it suits
Canonical MLOps suits teams operating machine learning workflows on Linux, self-hosted infrastructure, or the web. It is aimed at organizations needing open-source lifecycle tooling across varied deployment environments.
Plans and prices
Canonical MLOps support subscription
Open source stack · security maintenance · optional 24/7 support with SLAs
Read 5 Oct 2026 · canonical.com
On the record
- Automation
- The platform provides automated workflows, security patching, and tooling integrations for deploying and maintaining models.canonical.com · read 5 Oct 2026
- Cloud support
- Charmed Kubeflow supports AWS, Azure, and GCP Kubernetes services, including EKS, AKS, and GKE.canonical.com · read 5 Oct 2026
- Company
- Canonical says it has helped build the future of open source since 2004.canonical.com · read 5 Oct 2026
- Deployment
- Charmed MLflow can run on a workstation, CNCF-conformant Kubernetes, or a public cloud.canonical.com · read 5 Oct 2026
- Feast features
- Charmed Feast provides a centralized feature registry, offline and online stores, real-time and batch ingestion, and versioning for ML pipelines.canonical.com · read 5 Oct 2026
- Integrations
- Canonical lists Jupyter, Grafana, and Prometheus as tools to use with Charmed MLflow.canonical.com · read 5 Oct 2026
- Kubeflow
- Charmed Kubeflow deploys, scales, and manages AI workflows across clouds, virtual machines, or bare metal.canonical.com · read 5 Oct 2026
- MLflow features
- Charmed MLflow supports experiment tracking, reproducible projects, a model registry, and model deployment.canonical.com · read 5 Oct 2026
- Pricing limit
- The Canonical MLOps page describes a per-node, per-year subscription but does not state a price.canonical.com · read 5 Oct 2026
- Purpose
- Canonical MLOps is an open source platform for moving machine learning models from experimentation to production across the ML lifecycle.canonical.com · read 5 Oct 2026
- Security
- Canonical says its MLOps components receive up to 15 years of security maintenance, regular updates, and CVE fixes.canonical.com · read 5 Oct 2026
- Stack components
- Canonical’s MLOps stack includes Charmed Kubeflow, Charmed MLflow, and Charmed Feast.canonical.com · read 5 Oct 2026
- Support
- Optional 24/7 support with SLAs is available for Canonical’s MLOps stack.canonical.com · read 5 Oct 2026
