Check Out the First Data Science in Production Magazine
Taking models into production requires a professional workflow, high quality standards, and scalable code and infrastructure. This magazine is dedicated to reaping benefit from data by taking data driven applications into production.
Many organisations develop successful proof of concepts but then don’t manage to materialize the models beyond their laptops. Taking models into production requires a professional workflow, high quality standards, and scalable code and infrastructure. Data Science in Production is dedicated to reaping benefit from data by taking data driven applications into production.
Like the magazine? Leave your email address and we'll notify you when the next edition is published.
In This Magazine
In this first issue of Data Science in Production, Giovanni Lanzani discusses how organizations should avoid the Kaggle-curse in their journey to swiftly reach the production milestone. Henk Griffioen discusses the first step to get there: how to structure a (Python) project. Rodrigo Agundez deep dives into recently open-sourced Facebook Prophet, comparing it with his very own hand-crafted models (you will not believe what he found!).
Open source is also about giving back and that is what we try to do as much as we can: from the various Meetups we organize to the PyData Amsterdam conference. There is something, that is rareley emphasized: we contribute actively to the open source projects we love. In this first edition, we highlighted a few of our contributions to the open source world!
Hopefully, after reading this magazine, you are one step closer to becoming a Data Driven organization. Enjoy reading the articles and as always, we appreciate your feedback!
GDD @ NIPS 2017
December 12, 2017
Spark packages from a password protected Repository
December 11, 2017
GoDataDriven open source contribution: November 2017 edition
November 30, 2017
How to Decorate Your Scikit-learn Models Like a Christmas Tree
November 22, 2017
A practical example of generators and decorators in Python
November 17, 2017
"I Pity the fool", Deep Learning style
November 05, 2017