7 Best Practices for Deploying Data Products

In this whitepaper, 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 fundamental challenges.

Table of contents

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Intro | Mangement Summery
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Why is this so important to us?
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What does it mean to roll out a Data Science project?
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Deep Dive: Technical rollout of ML models
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Why is it so difficult to create added value with Data Science projects?
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Solutions and Best Practices
best practices for deploying data products

Why you should read it

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Unique

Get insights from going live on over 1.000 Data & AI projects over the last 7 years.
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Innovative

Artificial intelligence and machine learning are disruptive technologies. With our data products, you can revolutionize society.
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For free

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