Data Science Stack on Ubuntu
Set up ML environments with ease on your AI workstation using an out-of-the-box solution for data science.
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Get started with data science on your workstation or public cloud
Why choose Ubuntu for Data Science?
- Get started on your workstation to develop models. Scale as you upskill and deploy in production when needed using an MLOps platform.
- Benefit from long-term support (LTS), which is released every 2 years, with 5 years of standard support extended up to 15 years with an Ubuntu Pro Desktop subscription.
- Get security maintenance and enterprise-grade support for data science and ML packages such as Python, TensorFlow, PyTorch, MLflow, and more.
- Ubuntu is the target platform for NVIDIA AI Workbench and Canonical Data Science Stack. It enables accelerated data science workloads to run locally from multiple GPU silicon vendors, including NVIDIA and Intel.
Get leading open source ML tools seamlessly integrated
What is Data Science Stack?
Get started with data science using a few commands.
- Get an ML environment ready within minutes on any Linux distribution
- Streamline the complexity of GPU configuration and quickly attach it to run containerized workloads
- Manage multiple machine-learning environments with an intuitive CLI and UI
- Access leading open source ML tooling such as Jupyter Notebook or MLflow
What's inside Data Science Stack?
Data science stack includes tools that will help you get started easily:
- JupyterLab for ETL, model training, and experimentation
- MLFlow for experiment tracking and model registry
- ML frameworks by default, include PyTorch or TensorFlow
- GPU support for different types and easy enablement
Fully configure your chosen stack to your specific needs.
Why choose
Data Science Stack?
Improve developer productivity
Easy to use on any AI workstation
Run your ML workloads in a secure environment
Begin your AI journey on Ubuntu
One vendor to support your AI stack
Scale your AI workloads with an MLOps platform
Machine learning operations (MLOps) is a practice that enables data scientists and ML engineers to develop and deploy models in a reproducible and repeatable manner.
Canonical delivers an end-to-end open source MLOps platform that helps you streamline the entire machine learning life lifecycle. Our stack is based on Charmed Kubeflow, an enterprise-ready application for deploying, scaling, and managing AI workflows on any cloud.
Download the MLOps guide More on MLOpsGet security, stability, and long-term support for your AI and MLOps
Ubuntu Pro is Canonical’s comprehensive subscription for open source security, support, and compliance.
Ubuntu Pro provides security patching for all the popular open source software that data scientists rely on. It ensures that everything – from AI libraries to your infrastructure and underlying PostgreSQL, OpenSearch, and Kubeflow components – remain security maintained and supported, with up to 15 years of vulnerability fixes and long term support. The result? Easier management, more time to focus on building, and a faster path to stable, reliable, and compliant production MLOps.
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