PyData Berlin 2024 September Meetup

Welcome to the PyData Berlin September meetup!

We would like to welcome you all starting from 18:30. There will be food and drinks. The talks begin around 19.00.

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 can join as the space is limited.

Host:
SPICED Academy is excited to welcome you for this month's version of PyData. At SPICED Academy, we connect the next tech success stories to their futures. Our intensive bootcamps in Data Science, Data Analytics, UX/UI, Java Development and Web Development teach the most up-to-date, in-demand skills on the market.
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The Lineup for the evening

Talk 1: FAir Interpolation Method (FAIM) for Trade-Offs between Mutually Exclusive Algorithmic Fairness Criteria in Machine Learning and Law?
Abstract: Ensuring fairness in AI is crucial but challenging, as different fairness criteria often conflict. This talk introduces the FAir Interpolation Method (FAIM), a novel algorithm using optimal transport to balance competing fairness measures, such as ‘calibration within groups’ and ‘balance for the positive/negative class’. FAIM aims to adjust unfair predictions to meet a weighted combination of fairness standards, and its effectiveness is demonstrated with synthetic credit score data, COMPAS data, and e-commerce recommendations. FAIM could help align AI systems with legal standards, including recent EU regulations.
Speaker: Alex Loosley is a Responsible AI Scientist and Engineer with experience in developing algorithms for crop protection, document intelligence, and fashion fit recommendations. Outside of his AI work, he referees professional ice hockey in Germany.

Talk 2: AI on a Microbudget - Methods of Machine Learning Miniaturization?
Abstract: Current progress in AI has seen remarkable capabilities emerging from
simple prediction tasks – if we scale them massively. Surprisingly, we
get sparks of reasoning and intelligence in a model that was trained to
do little more than masked word prediction. Since that realization the
AI field has pursued ever larger models, trained at “eye-watering” cost.
If scaling is all you need – does it follow that, in practice, money is
all you need?
In this talk we explore ideas for the rest of us, the GPU-poor. Taking
examples from language processing and computer vision, we’ll show you
how to make do with less – less computing power, less person power, less
data – while still building powerful models. We will introduce a set of
methods and open source tools for the efficient reuse and
miniaturization of models, including transfer learning and fine-tuning,
knowledge distillation, and model quantization. Our talk aims to provide
an overview for ML practitioners, draws from our combined project
experience, and is accompanied by a repository of code examples to get
you started with building AI on a microbudget.
Speaker: Katharina Rasch is a data scientist and computer vision engineer with a PhD in Computer Science from KTH Stockholm. She currently freelances in Berlin and also works as a teacher.
Christian Staudt is a data scientist with 8 years of freelance experience. He focuses on machine learning from prototype to deployment, contributes to open source and has organized PyData community events.

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 :)

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