--- title: "Open source AI\n | Ubuntu" description: 'Open source AI: path to production for models. From development to deployment, get a seamless experience.' url: https://ubuntu.com/ai?format=md keywords: index, follow --- # Take your models to production with open source AI Build enterprise-grade AI projects with secure and supported Canonical MLOps. Develop on your Ubuntu workstation using Charmed Kubeflow or Charmed MLFlow and scale up quickly with open source tooling in every part of your stack. [Contact us](https://ubuntu.com/ai/contact-us) [Read our guide to MLOps](https://ubuntu.com/engage/mlops-guide) --- ## Why choose Canonical for Enterprise AI? * Reliable experts to speed up your AI journey * One vendor to support your AI stack * Run your workloads anywhere, including hybrid and multi-cloud * Simplified operations with lifecycle management and automation * Simple per node subscription --- ## Develop artificial intelligence projects on any environment --- ## Ubuntu: the OS of choice for data scientists Develop machine learning models on Ubuntu workstations and benefit from management tooling and security patches. [Read more about Ubuntu workstations ›](https://ubuntu.com/desktop/organizations) --- ## Move beyond experimentation with machine learning operations (MLOps) MLOps is the short term for machine learning operations and it stands for a set of practices that aim to simplify workflow processes and automate machine learning and deep learning deployments. MLOps is an approach that enables you to deploy and maintain models reliably and efficiently for production, at a large scale. [Learn more about MLOps](https://ubuntu.com/blog/what-is-mlops) ## Open source MLOps tooling --- ### Charmed Kubeflow Develop and deploy models with automated workflows. Charmed Kubeflow is an end-to-end MLOps platform designed to run AI at scale. It is the foundation of Canonical MLOps and seamlessly integrates with other big data and machine learning tools. --- [What is Kubeflow ›](https://canonical.com/mlops/kubeflow/what-is-kubeflow) ### Charmed MLflow Track your experiments and get a better overview of your model catalogue. Charmed MLFlow is an open source platform used for managing machine learning workloads. It integrates with other MLOps tools to cover different functions of the machine learning lifecycle. --- [Learn more about MLFlow ›](https://ubuntu.com/blog/charmed-mlflow-beta) ### Charmed Spark Simply the best way to run Spark®, whether on the cloud or in your data centre. Runs on Kubernetes. Includes a fully supported distribution of Apache Spark. --- [Learn more about Spark ›](https://canonical.com/data/spark) ### Charmed OpenSearch Charmed OpenSearch simplifies the operations of your favourite search and analytics suite. In addition, OpenSearch provides an integrated vector database that can support AI systems by serving as a knowledge base. --- [What is OpenSearch ›](https://canonical.com/data/opensearch/what-is-opensearch) --- ## Run AI at scale with Canonical and NVIDIA With NVIDIA AI Enterprise and NVIDIA DGX, Charmed Kubeflow improves the performance of AI workflows, by using the hardware to its maximum extent and accelerating project delivery. Charmed Kubeflow can significantly speed up model training, especially when coupled with DGX systems. --- --- * Quick deployment * Run the entire ML lifecycle * Composable architectures * Reproducibility, portability, scalability --- [Read our joint whitepaper ›](https://ubuntu.com/engage/run-ai-at-scale) --- ## Use modular platforms to run AI at the edge or in large clouds Production-grade projects require a solution that enables scalability, reproducibility and portability. Canonical MLOps speeds up AI project timelines, giving you: --- * The same experience on any cloud, whether private or public * Low-ops, streamlined lifecycle management * A modular and open source suite for reusable deployments [Read more about Edge AI ›](https://ubuntu.com/blog/edge-ai) --- ## Open source AI services ### Managed Canonical MLOps Focus on building production grade models, while Canonical experts manage the infrastructure underneath. --- * 99.9% uptime * 24/7 monitoring * High availability --- [Get the Managed MLOps datasheet ›](https://pages.ubuntu.com/rs/066-EOV-335/images/Charmed Kubeflow%20-%20Digital.pdf) --- ### AI consulting Work with our experts to understand your data better and deliver on your use case. --- * Data exploration workshop * Canonical MLOps deployment * MLOps workshop * PoC-based --- [Get the AI consulting datasheet ›](https://assets.ubuntu.com/v1/429359fe-MLOps%20advisory%20DS%2029.11.22.pdf) --- ### Support Looking for Kubeflow support? Work with our team to get support for any cloud environment or CNCF-complaint Kubernetes distribution. [Get the Charmed Kubeflow datasheet ›](https://pages.ubuntu.com/rs/066-EOV-335/images/Charmed Kubeflow%20-%20Digital.pdf) --- ## Open source AI resources ### [Case study: Real-time space tracking with AI supercomputing](https://ubuntu.com/engage/ai-ml-casestudy-tasmania) University of Tasmania (UTAS) modernised its space-tracking data processing with the Firmus Supercloud, built on Canonical's open infrastructure stack. --- ### [Guide to MLOps](https://ubuntu.com/engage/mlops-guide) Learn how to take models to production using open source MLOps platforms. --- ### [Guide: Run AI at scale with Canonical and NVIDIA](https://ubuntu.com/engage/run-ai-at-scale/) Learn how to scale AI projects using hardware that's designed for AI workloads and certified software. --- ### [Kubeflow vs MLFlow](https://ubuntu.com/engage/kubeflow-vs-mlflow) Choosing a suitable machine learning tool can often be challenging. Understand the differences between the most famous open source solutions. ## [Talk to our experts ›](https://ubuntu.com/ai/contact-us)