Hi PyLadies Berlin! In this members' talks evening held in-person and online, we will have an introduction from our sponsor Bettermile and 2 very insightful talks about Scikit Learn for you and a showcase of data at Bettermile 🥳
🦸🏻♀️ 🦹 Non Coding Super Powers is our beloved quick series about everything else, but code, that we need to be nurtured and grown.
🎤 Contributing to OpenSource - how to get started in 5 minutes!
💡 Stefanie Senger (she/her) - Scikit-learns metadata routing API
This talk will introduce scikit-learn users to the new API for metadata routing, a feature introduced in the recent releases and almost fully available since version 1.5 (released in May 2024).
Level: Beginner to the domain (already familiar with Python)
👩💻 Stefanie is an open source developer at :probabl., contributing mainly to scikit-learn. With a PhD in History centered on Solidarity Movements, she made a career shift into tech, bringing a unique interdisciplinary perspective to her work.
✨ Tamara Atanasoska (she/her) - Writing a custom scikit-learn estimator
Scikit-learn is a popular machine learning library. It currently has over 200 estimators ready to use for a vast array of use cases. What if you are working on something special that still hasn't found its way into the library? Scikit-learn offers a way to write new compatible estimators, which can be seamlessly integrated with the rest of the library. We will look into what an estimator is, what API that scikit-learn estimators have, reasons why you would like to implement your own and an example of how to. We will end with real-world examples of how other OSS projects use this for their needs.
Level: Intemediate Python developer
👩💻 Tamara is working on ML explainability, interpretability and fairness. She is a maintainer of fairlearn and a scikit-learn and skops contributor. Tamara works as an open source engineer at :probably..
📦 Helen FitzGerald and Claudia Stangarone - Predicting Delivery Risks with Machine Learning: A TrustCourier Innovation
Claims in logistics, especially for lost parcels or delivery issues, can be a significant cost for companies. In this talk, we’ll present the framework and share some early results of a new feature within TrustCourier. This feature uses machine learning to predict and flag high-risk deliveries before they escalate into costly claims.
👩💻 Claudia is a data analyst at GLS Studio, with a strong interest in machine learning and data engineering. With a PhD in mineral physics, she switched from a career in geo and planetary science a few years ago. Her goal is to strike the perfect balance between technology and science.
👩💻 Helen is a junior data analyst who transitioned into the field after achieving a B.A and M.Phil in linguistics, where she was first introduced to language models and Python. Her prior experience spans business development, social media & community management, and marketing, equipping her with a diverse and well-rounded perspective.
✨ Our host Bettermile provides a geo-based and AI-powered SaaS product suite for dynamic, multi-constraint, many-stop address processing, routing and navigation.
📆 Agenda
18h30 Doors open
19h00 Community Announcements
19h15 Welcome from our sponsor Bettermile
19h20 Non-Coding Super Power talk: contributing to open source
19h25 Scikit-learns metadata routing API
19:55 short break
20h00 Writing a custom scikit-learn estimator
20h35 Predicting Delivery Risks with Machine Learning: A TrustCourier Innovation
20h50 Networking
21h30 See You Next Time! :D
❓ 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.
🎥 This hybrid event will be streamed on YouTube https://www.youtube.com/@PyLadiesBerlin
🍕 There will be food and drinks (with vegetarian and vegan options thanks to our sponsor)
🤝 By attending our online event, you agree to the PyLadies Code of Conduct: https://www.pyladies.com/CodeOfConduct/
📧 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:
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