The potential of data science and machine learning is almost infinite, but getting started can often be overwhelming. There is a tremendous number of tools to use from, too many use cases to work on, and the costs involved have likely raised some eyebrows. AI enthusiasts are known for actually spending more time on setting up their ML environment than building models. They need to deal with tool incompatibility, versioning constraints and difficult upgrades.
This workshop by Canonical’s Michal Hucko will help you get started with data science within minutes at no cost. It will explore the initial steps anyone should follow before even starting, as well as how open source tooling such as MLflow or Jupyter Notebooks can be easily deployed and run. At the end of it, you will have a fully open source ML environment up & running, ready for you to build your first model. Learn more at https://canonical.com/solutions/ai and https://canonical.com/data Get started at https://documentation.ubuntu.com/data-science-stack/en/latest/
