Balancing accessibility against performance in a Windows-centric workplace.
As the prevalence of AI/ML in organisations continues to grow, accessibility is becoming an increasingly important factor in supporting data scientists working across a wide range of projects.
Ubuntu enables efficient command line workflows and a consistent development to deployment experience when leveraging Ubuntu in the cloud. As a target platform for many of today’s most commonly used machine learning tools, it is also backed by a wealth of supporting documentation and tutorials. However, the majority of enterprise data scientists work within a Windows-centric ecosystem. In these circumstances, centralised system management and Windows-specific productivity and collaboration tools can make the use of dedicated Ubuntu workstations impractical.
Windows Subsystem for Linux (WSL) aims to solve this problem by allowing data scientists to leverage Ubuntu workflows within the work environment of a Windows machine.
In this whitepaper we cover:
- A high level overview of data science workflows in the enterprise
- The technical design underlying both WSL 1 and WSL 2
- How interoperability and a shared filesystem deliver tight integration between Ubuntu WSL and Windows
- How to work with a variety of open source technologies while leveraging Ubuntu on your Windows systems
- Performance comparisons against bare metal Ubuntu for data science workloads
To find out more about Ubuntu WSL and how Canonical can support data scientists in your organisation, visit: