One-click registration

About the webinar

Ten years ago, data science was advertised as one of the most attractive jobs in the market. This interest has not fallen over time, so even nowadays data scientists are in the top 20 fast-growing occupations in the US according to the Bureau of Labour Statistics. This has led to more and more people pursuing data science as a career; however, these newcomers still face large obstacles as they start their data science journey. Some of the most common challenges include:

• Tool fragmentation: Data scientists and ML engineers spend often more time on preparing their environment than building models. This includes tasks such as tool deployment and integration, GPU configuration, and packaging dependencies.
• Initial investment: Data scientists – especially ones early in their data science or ML journey – are looking to lower their costs. The cost-versus-career-stability factor is a significant challenge for young talent, and so controlling their project budget is often a make-or-break issue for their long-term career.
• Security concerns: ML projects access a high number of packages which can often include vulnerabilities that could put at risk the functionality of the workstation, the personal data of the user and more. Running in a secure environment protects data scientists from cyberattacks, data breaches, and other security risks.

Data science tools in the open source space

Machine learning (ML) came overall with a shift in the tech space. There is a wide range of open source tools, frameworks, and libraries. From the operating system to the cloud-native applications, users can run a fully open source stack to develop their ML models.

Data Science Stack (DSS) is an out-of-the-box solution for data scientists and machine learning engineers, published by Canonical. It is a ready-made environment for ML enthusiasts that enables them to develop and optimise models without spending time on the necessary underlying tooling. It is designed to run on any AI workstation that runs Ubuntu, maximising the GPU’s capability and simplifying its usage.

Join our webinar to learn more about DSS

Join us in our next webinar to learn more about data science tools, with a focus on DSS and its capabilities. During the webinar, Michal Hucko, MLOps engineer at Canonical and Andreea Munteanu, AI Product Manager, will talk about:

• Key considerations when getting started with data science
• Data science through the open source lenses
• Deep dive into Data science stack (DSS)
• Demo of the DSS

Prepare your questions and join us live to get insights into how DSS improves the developer experience of Ubuntu users who are active in the data science or ML space.