Data Science in Production Magazine Available
Dedicated to take data-driven applications into production.
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.
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 and Dutch Data Science Week. There is something, that is rarely 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!