PyData Berlin 2024 November Meetup

Welcome to the PyData Berlin November 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:
Bonial is excited to welcome you to this month's version of PyData.
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The Lineup for the evening

Talk 1: Running Python data transformations at scale with dbt and Astronomer Cosmos
Abstract: I will discuss how we built a tool using dbt and Astronomer Cosmos to orchestrate Python data transformations at scale. As part of a global team, we faced the challenge of developing and scaling data transformations for our entities across more than 50 countries. Our data scientists write these transformations to improve our machine learning models, and the need to manage such a large number of entities required an efficient and scalable solution. This tool streamlines the entire process, enabling data scientists to quickly develop data transformations, leverage built-in dbt tests for data validation, and seamlessly deploy these transformations to production environments. The integration of dbt and Astronomer Cosmos has significantly accelerated our workflows, ensuring robust and scalable data operations while also empowering our data scientists to deliver more value, faster.

Speaker: Galuh Sahid currently works as a Senior Machine Learning Engineer at Delivery Hero. She is also recognized as a Google Developer Expert in Machine Learning. She previously worked at Twitter and Gojek. She has developed and productionized various ML applications, including fraud detection, content moderation, and marketing using traditional ML, NLP, and computer vision. In her free time, Galuh enjoys painting and hiking.

Talk 2: Anomaly Detection in Track Scenes
Abstract: In the “Digitale Schiene Deutschland” initiative, Deutsche Bahn is developing an automated train driving system. To support this, we collaborated with them to create a machine learning solution that detects anomalous objects on and around tracks using onboard RGB cameras. Rather than recognizing specific object classes (e.g., people, signals), this system identifies any object and ranks it by anomaly. This presentation covers challenges, approaches, and the final solution: a unique pipeline using multiple machine learning components, including monocular depth estimation, segmentation, image embedding, and anomaly detection. The OSDAR23 dataset, containing 45 scenes with RGB, infrared, radar, and lidar data, aids in model finetuning and evaluation. Additionally, unannotated data was used for self-supervised learning.

Speaker: Maximilian Trescher studied physics in Berlin and Paris, PhD in theoretical physics in 2018 (Freie Universität Berlin). Then he worked 4 years (18-22) as a software engineer (Java, databases etc).
Since 2022 he is a machine learning Scientist at dida (www.dida.do).

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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