I remember a CS professor of mine pointing out that most of the magic in Harry Potter can now be done on computers! Images dance on our digital newspapers. Cellphones swirl with memories like portable Pensieves. Computer classes are our Charms. Algorithms are our Arithmancy!

If computing departments are the new Hogwarts, then technical tomes are the new spell books. The best works brim with technical secrets and arcana, and represent a totem to some branch of our magical field: Python. Algorithms. Visualization. Machine learning.

I'm therefore particularly excited and proud to share that Streamlit has a major new book, lovingly written by one of our own Streamlit Creators (and Facebook data scientist) Tyler Richards.

This is a true spell book. Yes, other books teach Streamlit, but this is the first which captures the essence of Streamlit. The book demonstrates how Streamlit is transforming the very definition of data science and machine learning.

Throughout the 2010s, data science and machine learning had two basic outputs. On the one hand, you could use a notebook environment to create static analyses. On the other, you could deploy complete machine learning models into production. Streamlit opened up a new middle way between these two: interactive apps that let you play with analyses and share models interactively throughout an organization.

Getting Started with Streamlit for Data Science teaches you how to master this new superpower. You start by creating a basic analysis and work your way up to complete Streamlit apps with fancy graphics and interactive machine learning models.

So go pick up your copy! (Starting August 21, you can even use discount code 25TYLER on Amazon.) Learn the deep secrets of Streamlit. Join our magical community. Share your apps with the world. Contribute to our gallery. Or invent your own spells with custom components. Whether you're a wizard-in-training looking to deploy your first machine learning project or an experienced auror, this book will turn you into a Streamlit sorcerer. 🧙🏽‍♂️