Whitepaper - 7 best practices for rolling out
from Data Products

In this white paper, we show the reasons why so many data science projects currently fail in the deployment phase. We define deployment as the moment when a proof of concept or pilot project becomes a data product to be integrated into business operations. We looked at different variants of technical deployment of data science projects and identified five basic challenges.

Content of the white paper


Intro | Mangement Summery

Why is this so important to us?
What does it mean to roll out a data science project?
Deep Dive: Technical roll-out of ML models
Why is it so difficult to create added value with data science projects?
Solutions and best practices
Rolling out Data Products_Whitepaper

Your added values



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7 Best Practices Whitepaper



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