talk-data.com

Welcome to the PyData Berlin May meetup!

We would like to welcome you all starting from 18:45. There will be food and drinks. The talks begin around 19.30 and the doors will close at 19:30. Make sure to arrive on time!

Please provide your first and last name for the registration because this is required for the venue's entry policy. If you cannot attend, please cancel your spot so others are able to join as the space is limited.

Host: Ecosia is excited to welcome you to this month's version of PyData.

Entrance is in Hof 4 - there will be signs - then up to the 3rd floor of the building.

**************************************************************************

The Lineup for the evening

Talk 1: Specializing Small Language Models With Less Data Abstract: I will present a practical, end-to-end solution for training SLMs using synthetic data, covering key aspects from data curation through training to model evaluation. You will leave with concrete strategies for building efficient, domain-specific language models for production environments. Most AI teams are exploring the possibilities of LLMs rather than being focused on margins, but soon, efficiency will become important. Small, specialized language models (SLMs) offer a promising alternative, but training them requires extensive manually-labeled datasets - a significant engineering bottleneck. In this talk, I will discuss how large language models can be used to help generate and curate the data needed for SLM training. Using extractive question answering as a case study, We'll examine how this approach can dramatically reduce data collection time while maintaining model performance.

Speaker: Jacek Golebiowski Bio: Jacek is the CTO of distil labs, building specialised AI agents that can be deployed on-device/on-prem with minimal data. Before that, he was a machine learning team lead at AWS, focused on Automated ML and natural language processing. He holds a PhD in Machine Learning for Quantum Mechanics from Imperial College London.

--- Talk 2: Exploring fairlearn and practical strategies for assessing and mitigating harm in AI systems Abstract: As AI becomes a more significant part of our everyday lives, ensuring these systems are fair is more important than ever. In this session, we’ll discuss how to define fairness and the potential harms our algorithms can have on people and society. We'll introduce fairlearn, a community-driven, open-source project that offers practical tools for assessing and mitigating harm in AI systems. We’ll also explore how to discuss bias, different types of harm, the idea of group fairness and how they all relate to fairlearn's toolkit. To make it all concrete, we’ll walk through a real-world example of assessing fairness and share some hands-on strategies you can use to mitigate harm in your own ML projects.

Speaker: Tamara Atanasoska Bio: Tamara is a software engineer, OSS contributor and maintainer and NLP researcher.

--- Lightning talks There will be slots for 2-3 Lightning Talks (3-5 Minutes for each). Kindly let us know if you would like to present something at the start of the meetup :)

*** NumFOCUS Code of Conduct

THE SHORT VERSION Be kind to others. Do not insult or put down others. Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate for NumFOCUS. All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery are not appropriate. NumFOCUS is dedicated to providing a harassment-free community for everyone, regardless of gender, sexual orientation, gender identity, and expression, disability, physical appearance, body size, race, or religion. We do not tolerate harassment of community members in any form. Thank you for helping make this a welcoming, friendly community for all.

If you haven't yet, please read the detailed version here: https://numfocus.org/code-of-conduct ***

PyLadies Berlin are excited to bring you this open source workshop dedicated to setting up your scikit-learn or fairlearn development environment.

scikit-learn is a popular machine learning library and is widely adopted in industry as well as academia.

Fairlearn is a community-driven project to help data scientists improve fairness of AI systems.

This is a warm-up session for the upcoming workshop, but it is open to anyone who would like to get guidance, even if you won’t attend the workshop. No prior contributing experience required!

By setting up your development environment in advance, you can use the in-person workshop time for finding an issue to work on, and working on a contribution.

• Format for the session: First 10 minutes : welcome and introduction The rest : "office hours" during which you can ask questions and where you'll be supported with setting up a development environment.

• Preparation work for scikit-learn To get the most out of the session, it's encouraged that you check out the Developer's Guide of scikit-learn and follow steps 1 to 7 under the section "How to contribute": https://scikit-learn.org/dev/developers/contributing.html#how-to-contribute. You will also find a lot of useful additional information on this page, for example video resources: https://scikit-learn.org/dev/developers/contributing.html#video-resources. Please be aware that it could take longer to set up a development environment on ​​a computer running a Windows operating system compared to MacOS or Unix. If you are using Windows, it is recommended to install Windows Subsystem for Linux. You can find instructions on the installation process for example by following the steps described here: https://learn.microsoft.com/en-us/windows/wsl/install. Please note that you should be using WSL 2, and here's how you can upgrade to this version if you have WSL 1: https://learn.microsoft.com/en-us/windows/wsl/install#upgrade-version-from-wsl-1-to-wsl-2

• Preparation work for Fairlearn You don't need to do any preparation work for Fairlearn for this session, instructions will be given during this event. However, it is recommended that you have a look at the Fairlearn Contributor Guide here: https://fairlearn.org/v0.12/contributor_guide/index.html

• How to join We'll be using Discord for this event. Please follow the steps below: 1. Join the scikit-learn Discord server: https://discord.gg/aBgkfXBtWZ 2. Join the #help-desk-voice channel. This is the channel where we'll be hosting this workshop.

• Audience level Everyone is welcome to attend this session! If you've never contributed to open source software before, then you will learn how to, and if you have experience contributing, then you can either help mentor other attendees or you can work on more challenging contributions. It is useful to have some scikit-learn, git, and python experience.

• Facilitators The session will be lead by Maren Westermann (PyLadies Berlin, scikit-learn team member), Tamara Atanasoska (scikit-learn contributor, Fairlearn maintainer), Stefanie Senger (scikit-learn team member), Adrin Jalali (scikit-learn and Fairlearn maintainer) and Guillaume Lemaitre (scikit-learn maintainer).

• By attending our event, you agree to the PyLadies Code of Conduct: https://www.pyladies.com/CodeOfConduct/

❓ Can men attend ❓ Everyone is welcome. If you identify as someone well-represented in open source and in tech, please be mindful of the space and privileges you have, and use it to support others.

• Contact Interested in speaking at one of our events? Have a good idea for a Meetup? Get in touch with us at [email protected] Find us on the PyLadies Global workspace: 1. https://slackin.pyladies.com enter your email address. 2. Accept the email invitation 3. Go to workspace https://pyladies.slack.com 4. Join channel #city-berlin\, #germany\, #jobs-europe

In this podcast episode, we talked with Tamara Atanasoska about ​building fair AI systems.

About the Speaker:​Tamara works on ML explainability, interpretability and fairness as Open Source Software Engineer at probable. She is a maintainer of fairlearn, contributor to scikit-learn and skops. Tamara has both computer science/ software engineering and a computational linguistics(NLP) background.During the event, the guest discussed their career journey from software engineering to open-source contributions, focusing on explainability in AI through Scikit-learn and Fairlearn. They explored fairness in AI, including challenges in credit loans, hiring, and decision-making, and emphasized the importance of tools, human judgment, and collaboration. The guest also shared their involvement with PyLadies and encouraged contributions to Fairlearn. 00:00 Introduction to the event and the community 01:51 Topic introduction: Linguistic fairness and socio-technical perspectives in AI 02:37 Guest introduction: Tamara’s background and career 03:18 Tamara’s career journey: Software engineering, music tech, and computational linguistics 09:53 Tamara’s background in language and computer science 14:52 Exploring fairness in AI and its impact on society 21:20 Fairness in AI models26:21 Automating fairness analysis in models 32:32 Balancing technical and domain expertise in decision-making 37:13 The role of humans in the loop for fairness 40:02 Joining Probable and working on open-source projects 46:20 Scopes library and its integration with Hugging Face 50:48 PyLadies and community involvement 55:41 The ethos of Scikit-learn and Fairlearn

🔗 CONNECT WITH TAMARA ATANASOSKA Linkedin - https://www.linkedin.com/in/tamaraatanasoska GitHub- https://github.com/TamaraAtanasoska

🔗 CONNECT WITH DataTalksClub Join DataTalks.Club:⁠⁠https://datatalks.club/slack.html⁠⁠ Our events:⁠⁠https://datatalks.club/events.html⁠⁠ Datalike Substack -⁠⁠https://datalike.substack.com/⁠⁠ LinkedIn:⁠⁠  / datatalks-club  

​Building fair AI systems -Tamara Atanasoska

​Outline:

1. ​Linguistics and Language in Technology
2. ​Fairness in AI Systems
3. ​Fairlearn

​About the speaker: ​Tamara works on ML explainability, interpretability and fairness as Open Source Software Engineer at :probabl.. She is a maintainer of fairlearn, contributor to scikit-learn and skops. Tamara has both computer science/ software engineering and a computational linguistics(NLP) background.

Join our slack: https://datatalks.club/slack.html