PyData Prague #18 - A Vector from Lab to Hub

Hello Python enthusiasts and vector entertainers!

The 18th Prague PyData meetup will take place at Pure Storage offices. As usual, the talks will start at 18:30 but we encourage you to come as soon as 18:00 to enjoy the opportunity to socialize and refresh yourselves (which you can continue doing during the break and after the talks). Note that this time we will want you to sign a non-disclosure agreement (NDA) when entering (only to protect the Pure Storage hardware in development; the talks themselves are public of course!)

Our main goal is to build the community around Python and data and make it welcoming to people of various skills and experience levels.

⚡ If you are interested in giving a lightning talk (up to 5 minutes to present an idea, tool or results related at least to some degree to Python and/or data), please contact us before the event or at its beginning.

📢 Unlocking Efficiency - The Power of Vectorization
(Milan Ondrašovič, Rossum)

In this talk, we will break down a crucial yet often overlooked skill for ML engineers and data scientists: code vectorization, the cornerstone of modern numerical libraries. The aim is to show when and how to apply this technique to significantly boost performance. We will provide practical insight on implementation, discuss pros and cons, and explore the impact on the codebase. Using primarily Python and NumPy, our code examples will demonstrate the portability of vectorized solutions across libraries and languages.

📢 Jupyter(Hub/Lab) - Journey from On-prem to AWS
(Jakub Hettler, Alma Career)

Let’s have a look at how we @AlmaCareer Czechia Business Intelligence team moved JupyterHub and JupyterLab from on-premise infrastructure to AWS. Why we used Amazon Sagemaker Studio for just 3 weeks and why we are happy with Jupyter running on top of Coder (coder.com) in AWS at the end. Infrastructure point of view with deeper dive into pros/cons of on-prem JupyterHub/Lab on Hashicorp Nomad, Amazon Sagemaker Studio and Coder. All this considering the requirements of 20 working users in JupyterLab.