MLflow made easy
A seamless start to your machine learning journey with open source
Charmed MFlow is a lightweight and secure machine learning platform for any developer. It can be deployed on your infrastructure of choice, and is backed by expert support.
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Get the features ofupstream MLflow
Charmed MLflow, Canonical's distribution of the upstream project, comes with all the upstream features, including:
- Experiment tracking
- Reproducible projects
- Model registry
- Models deployment
Record and query experiments: code, data, config and results.
Package data science code in a format that enables reproducible runs on any platform.
Store, annotate and manage models in a centralised repository.
Deploy machine learning models in diverse serving environments.
With the peace of mind of a secure and supported solution
In addition to upstream features, Charmed MLflow includes:
- Integration with machine learning and big data tools
- Simplified deployment on any infrastructure
- Security patching
- Bug fixing
Use Charmed MLflow with
Deploy easily on any infra, from workstations to public clouds
Run Charmed MLflow on any environment. The machine learning platform is made for everyone – from enthusiasts who are just getting started to enterprises running workloads at scale.
- Quickly deploy it on a workstation
- Deploy on any CNCF-conformant Kubernetes
- Run it on a public cloud
MLflow services
Managed services
We manage your MLflow deployments on any cloud, including: automatically deploying, patching, optimising and upgrading with our open source operator.
- Backed by a Service Level Agreement (SLA)
- Low cost of ownership
- Expert help in application operations
- 24/7 support included
Enterprise support
We provide 24/7 phone and ticket support for your MLflow deployments on any environment with an Ubuntu Pro + Support subscription.
- Backed by an SLA
- Quickly resolve technical support issues
- Expert help in applications operations, troubleshooting and bug fixes
MLflow resources
Read more about Charmed MLflow's capabilities and follow our tutorials.
Understand the differences between the upstream project and Canonical's distribution.
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