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.

Top 5 reasons to use Ubuntu for your AI/ML projects ›

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

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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.

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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.