--- title: MLOps description: The open source MLOps platform that seamlessly integrates open source tooling to cover all stages of the machine learning lifecycle. url: https://canonical.com/mlops?format=md keywords: open source mlops --- # Open source MLOps for AI that scales Move from experimentation to production using a trusted, open source MLOps platform. Take the complexity out of deploying and maintaining your models with automated workflows, security patching, and tooling integrations that span the entire end-to-end machine learning lifecycle. [Access the MLOps toolkit](https://ubuntu.com/engage/mlops-toolkit) [Get in touch](https://canonical.com/mlops/contact-us) --- --- ## Why choose Canonical for open source MLOps? Up to 15 years of security maintenance --- Automated lifecycle management --- End-to-end tooling integration --- Deploy on any public or private cloud --- Simple per node support subscription --- ## What is MLOps? Machine learning operations (MLOps) is like DevOps for machine learning. It is a set of practices that automates machine learning workflows, ensuring scalability, portability, and reproducibility. [Get the full guide to MLOps ›](https://ubuntu.com/engage/mlops-guide) --- ## End-to-end open source MLOps Canonical's MLOps stack delivers all the open source solutions you need to streamline the complete machine learning lifecycle. These tools are tightly integrated to ensure a smooth MLOps journey, from experimentation to production. ### Charmed Kubeflow Charmed Kubeflow is the foundation of Canonical MLOps. It is an enterprise-ready platform for deploying, scaling, and managing AI workflows on any cloud. [Learn more about Charmed Kubeflow](https://canonical.com/mlops/kubeflow) --- ### Charmed MLFlow Charmed MLFlow is our solution for managing the model lifecycle. Track your experiments, package code in a reproducible format, and store and deploy models - all using a lightweight platform that can be deployed on any infrastructure. [Learn more about Charmed MLFlow](https://canonical.com/mlops/mlflow) --- ### Charmed Feast Charmed Feast is an enterprise-grade feature store that enables you to bridge the gap between data engineering and model deployment. Native integration with Charmed Kubeflow ensures a seamless experience. [Learn more about Charmed Feast](https://canonical.com/mlops/feast) --- ## Fully open source, fully supported Each component of Canonical's MLOps stack is fully open source, and backed by long-term, enterprise-grade security maintenance and support commitments: * Up to 15 years of security maintenance * Regular updates and CVE fixes * Optional 24/7 support with SLAs The entire MLOps platform is covered by a simple per node, per year subscription. [Explore enterprise support](https://ubuntu.com/pro) --- ## What customers say --- *“We wanted one partner for the whole on-premise cloud because we're not just supporting Kubernetes but also our Ceph clusters, managed Postgres, Kafka, and AI tools such as Kubeflow and Spark. These were all the services that were needed and with this we could have one nice, easy joined-up approach.”* **Michael Hawkshaw** IT Service Manager European Space Agency [Get the full case study ›](https://canonical.com/case-study/esa) --- *“Partnering with Canonical lets us concentrate on our core business. Our data scientists can focus on data manipulation and model training rather than managing infrastructure.”* **Machine Learning Engineer** Entertainment Technology Provider [Get the full case study ›](https://ubuntu.com/engage/machine-learning-media-entertainment) --- ## MLOps services Canonical's experts deliver a range of services to help you move faster and smarter with AI projects. ### Kick start your AI/ML journey with an MLOps Workshop Build your tailored MLOps architecture in just 5 days. In a custom workshop, we'll help you design AI infrastructure for any use case and level up your in-house expertise to accelerate your machine learning initiatives. [More on MLOps workshops ›](https://canonical.com/mlops/mlops-workshop) --- ### MLOps Consulting Move faster with Canonical's MLOps consulting services. Our experts can design and deploy your full stack AI environment from the ground up on your substrate of choice. [Discover our consulting services ›](https://canonical.com/consulting) --- ### Managed MLOps Let us run the platform so your team can focus on developing and deploying models. Streamline operational service delivery and offload the design, implementation and management of your MLOps environment. [Talk to us about managed MLOps ›](https://canonical.com/mlops/contact-us) --- ## Open source MLOps resources ### [Data & AI solutions](https://canonical.com/solutions/data-and-ai) There's no machine learning without data. Explore our solutions that bridge the gap between data and AI. --- ### [Guide to MLOps](https://ubuntu.com/engage/mlops-guide) Learn how to take your models to production using open source MLOps platforms in this whitepaper. --- ### [What is MLOps?](https://ubuntu.com/blog/what-is-mlops) Read the blog to dig deeper into the fundamental principles of MLOps. Close ### Talk to our MLOps experts --- Looking to scale your MLOps infrastructure or need consulting services to kick start your AI journey? Our experts are here to help you.