
What weβre about
β₯οΈ This meetup group is run by 100% volunteers who support the Seattle open source community. β¨ Weβre a committed team, pouring our hearts into every event, made possible by the incredible generosity of our sponsors and their in-kind donations.πOur meetup group exclusively welcomes in-kind donations and does not accept monetary contributions. Despite our very limited resources, weβre always striving to make each event better than the last! π₯³
π·Your reviews truly mean so muchβthey help us keep uplifting the incredible open source community and secure more in-kind donations!"
Mission: Our mission is to support and promote women and non-binary individuals who are practicing, studying or are interested in the fields of machine learning and data science.
WiMLDS is a community of women* interested in machine learning and data science. We host events which include talks by prominent data scientists, lightning talks, technical workshops and networking events. Our members include engineers, technologists, statisticians, students and many other professionals who work in data science or would like to transition into this field.
We create opportunities for women* to engage in technical and professional conversations in a positive, supportive environment by hosting talks by prominent female and non-binary data scientists, technical workshops, networking events and hack-a-thons. We are inclusive to anyone who supports our cause regardless of gender or technical background. However, in support of our mission, priority for certain events and opportunities will be given to women*.
β’ Follow us (https://twitter.com/WiMLDS_Seattle) on Twitter (or https://twitter.com/WiMLDSΒ for the general account) or visit http://wimlds.org to learn about our chapters in other cities.
β’ Women & non-binary folks are invited to join the global WiMLDS Slack group by sending an email to slack@wimlds.org.
* individuals who identify as female, non-binary, genderqueer, genderfluid, agender, and all minority genders
π¨ Attention
- Due to restrictions at our venue, strictly no children.
Code of Conduct
By being a member of **WiMLDS **and/or attending our events, you are agreeing to abide by WiMLDS Code of Conduct.
WiMLDS reserves the rights to create events and make changes at any time and date without prior notice.
Our Code of Conduct ( https://github.com/WiMLDS/starter-kit/wiki/Code-of-conduct ) is available online and applies to all our spaces, both online and off.
Content Disclaimer: All information provided in and by https://www.meetup.com/seattle-women-in-machine-learning-and-data-science/ and it's organizers is provided for information purposes only. Information is subject to change without prior notice. We make no guarantees of any kind including but not limited of either expressed or implied, concerning the accuracy, completeness, reliability, or suitability of the information. Any links to external websites of information provided or returned from web search engines are provided as a courtesy. They should not be construed as an endorsement by this entity of the content or views of the linked materials.
Our events may be recorded and shared publicly with others. By attending this event, you consent to being photographed, filmed, or recorded for promotional purposes.
Upcoming events
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β’OnlineTeaching Computers to Read: Dataset Curation Impact on Model Performance
OnlineWorkshop Summary: Successful AI solutions arenβt about chasing the newest model - itβs about solving the right problems in the right way. The book βTeaching Computers to Readβ (out November 5 from CRC Press) focuses on what technical teams need to design, develop, deploy, and maintain useful NLP and AI solutions. Drawing on real-world experience and examples, the book offers actionable best practices to deliver adaptable, reliable AI systems that address business challenges with lasting, tangible value. In this tutorial, we will walk through one part of the Code Companion for the book. We will review the corpus distribution and variation, our annotated data distribution, and explore how our curated datasets impact the performance of different technical approaches, using information extraction as an example. The concepts covered in the tutorial are covered in more detail in the book, and there are additional exercises in the Code Companion for those interested in going beyond the tutorial session.
Prerequisites: To follow along, the prerequisites include cloning the repo from the "Teaching Computers to Read" Code Companion. Follow the 3 steps in the "Setup" section to clone the repo, create a virtual environment, install requirements, and download the relevant files (linked in the Readme to a HuggingFace dataset).
Bio: Rachel Wagner-Kaiser has 15 years of experience in data and AI, entering the data science field after completing her PhD in astronomy. She specializes in building NLP solutions for real-world problems constrained by limited or messy data. Rachel leads technical teams to design, build, deploy, and maintain NLP solutions, and her expertise has helped companies organize and decode their unstructured data to solve a variety of business problems and drive value through automation. Rachel is also the author of the book "Teaching Computers to Read".
Website: Personal
LinkedIn: LinkedIn22 attendees
Past events
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![PyData Seattle Conference 2025 π π by NumFOCUS [Community Partner]](https://secure.meetupstatic.com/photos/event/8/1/1/highres_531002065.jpeg)
