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.
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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 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!
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