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Set up ML environments with ease on your AI workstation with an out-of-the-box solution for data science on Ubuntu
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https://docs.google.com/document/d/1GdVmQD9EDf_ksN6r1qWTYSwvdynCzAE8wtKaEdtzDo8/edit
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Set up ML environments with ease on your AI workstation using an out-of-the-box solution for data science. Get started with data science using a few commands. Data science stack includes tools that will help you get started easily: Fully configure your chosen stack to your specific needs. 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
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.
Charmed Kubeflow is an MLOps platform that covers the entire ML lifecycle. It is a cloud-native application that runs anywhere, whether in a private or public cloud, supporting even hybrid or multi-cloud scenarios.
Learn how to select your data science tools and quickly get your environment ready on Ubuntu. Learn how to upgrade your data science workflows with WSL.
Choosing a suitable machine learning tool can often be challenging. Understand the differences between the most famous open source solutions.
Be on top of the security risks for machine learning projects and learn how to mitigate them.
Discover how managed AI infrastructure can accelerate deployments and increase security in our latest executive guide.
Data Science Stack on Ubuntu
Try it out
Watch the webinar to learn more ›
Get started with data science on your workstation or public cloud
Why choose Ubuntu for Data Science?
Get leading open source ML tools seamlessly integrated
What is Data Science Stack?
What's inside Data Science Stack?
Why choose
Data Science Stack?
Scale your AI workloads with an MLOps platform
Download the MLOps guide
What is Kubeflow?
Open source AI resources
Upgrade your data science workflows with Ubuntu WSL
Kubeflow vs MLFlow
An overview of machine learning security risks
Guide to Managed AI Infrastructure