Hey there,
Today I’m sharing with you a ML book deal that’s too good to miss out on, and reflections on PyData London last weekend.
This week I’ve finally got around to exploring running LLMs locally — unfortunately I’ve run into some limitations with my laptop even with the smallest of ollama models…! But I was impressed how easy it was to get started! I have some work arounds in progress to get this working and I can’t wait to share my upcoming projects once they are up and running! 👀
🚀 PyData London
This year has been a year of conferences for me. After an extended break from organising community events, I went full steam into organising Field of Play, a brand new Sports Data conference back in March, and then helped with PyData London!
It was my first time attending a PyData conference too and I was totally blown away. The atmosphere was unlike any conference I’ve been to before! Everyone was incredibly friendly and welcoming, the tutorials, talks and keynotes were thought-provoking and the food was absolutely fantastic (10/10)! There were so many talks in parallel sessions, it was a real challenge to choose which to attend. Fortunately they were recorded so I’ll be catching up on the ones I missed when they are released!
PyData is a global community organised by NumFOCUS who support education and open source tooling in data. If you’ve ever used pandas, numpy, matplotlib, or any of the tools here, then you’ve used an open source project supported by NumFOCUS! There are also monthly PyData meet-ups all over the world, bringing people together to share their ideas and expertise.
I came away from the conference inspired by the insightful talks I attended and energised by the meaningful connections I made over the weekend. I have a whole new list of ideas and projects and my calendar is filling up with virtual coffee chats to continue the conversations started at the conference. If you’re looking to meet other people in data and feel part of a community then I encourage you to get involved with PyData!
🔗 Resources & Links
As promised, here’s some resources not to miss out on!
✨ Machine Learning Mastery Bundle: There’s 1 day left on the Humble Bundle ML and AI book collection with 17 O’Reilly books including AI Engineering by Chip Huyen (which has been on my TBR list for a while)! Pay what you wish, starting at £18.62, making it an absolute bargain, and part of the payment is supporting Code For America.
📖 Data Dictionary updates: since my last email, a few new terms have been added to the Data Dictionary. Have you ever wondered what the difference between a Data Warehouse and Data Lake is? Go check out the definitions to find out!
💜 Feedback
I’m interested in finding out about the resources you are using for learning and staying up to date with the latest news in data. Fill in this 2 minute anonymous form to let me know! I’ll then compile a list of the books, podcasts and other resources shared!
Thank you for reading this newsletter! If you purchase the Humble Bundle ML books, let me know which books you find most valuable!
Until next time,
Kerry