PyData Berlin 2023 May Meetup

Welcome to the wonderful May of Pydata Berlin meetup !!

For everybody to feel safer, we recommend you test yourself against COVID-19 before coming to the event. A self-test or a rapid antigen test would suffice. And please refrain from coming to the event if you feel unwell.

we would like to welcome you all starting from 18:45 hrs. The Talks would begin 19:10 hrs

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The Lineup for the Evening

Talk 1: Bayesian A/B Testing - Is it worth it?

Speaker: Jakob Willisch https://de.linkedin.com/in/jakobwillisch

Abstract: Discussions around Bayesian vs. frequentist statistical paradigms can get philosophical very quickly. In this talk, I will briefly introduce Bayesian Hypothesis testing in the context of A/B testing and will highlight practical benefits and drawbacks we found when explored switching from a frequentist hypothesis testing approach.

Talk 2: Thinking outside of the Euclidean Space: Graph Machine Learning with Fastgraphml

Speaker: Sachin Sharma https://de.linkedin.com/in/sachin-sharma-4198061a9

Abstract: Graphs are everywhere, in fact we are surrounded by graph data for e.g. Internet of Things, 3D Point Clouds, Social networks, Event Graphs, Chemical Molecules, etc; where nodes represent real world entities and edges are associated with relationships between those entities. Today, many real-world applications are backed by Graph ML to run their prediction services. For example, UberEats leverages Graph ML to suggest to its users the dishes, restaurants, and cuisines they might like next. Pinterest uses Graph ML to make visual recommendations, and Google Deep Mind exploits Graph ML to make traffic predictions Graphs as a unique non-euclidean data structure cannot be operated by CNNs and require a special method to handle its non-regular structures which led to the recent progress in the area of Graph Neural Networks (GNNs). In the machine learning universe graph data structures are exploited by the Graph Neural Networks (GNNs) to perform the tasks of node classification, link prediction, graph classification/generation and clustering. Given the rise of this fascinating field, we have developed fastgraphml package (built on top of PyG) that can help users to build Graph ML models with just 3 lines of code. The first release is focused on providing Graph Embeddings (or node embeddings) as functionality as it acts as a foundation for all the Graph ML problems (node classification, link prediction, graph classification). In addition, the framework uses ArangoDB (the next-generation graph data and analytics platform) as a backend to export graphs directly into the fastgraphml package. Therefore, in this session, audience will deep dive into the universe of GraphML, its applications and how to build GraphML models with just 3 lines of code using fastgraphml.

There will be slots for 2-3 Lightning Talks (5-6 Minutes for each).
Kindly let us know if you would like to present something
info@pydata.berlin

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