Data Science Development Lifecycle – Everyone Talks About It, Nobody Really Knows How to Do It and Everyone Thinks Everyone Else Is Doing It

date:

November 19, 2019

Time:

10:05 am

Summary:

Data science is rapidly becoming the primary catalyst for product innovation. However, most of the projects are stuck in the Proof-of-Concept (POC) phase. Christian and René had the chance to be part of GfK’s journey from a traditional market research company to a prescriptive data analytics provider. In order to build end-to-end data-driven products successfully, it is necessary to blend what existing frameworks like SCRUM and CRISP provide with the best practices from software engineering. You will learn about how they gradually established a data science development lifecycle that overcomes the POC-trap by considering production realities from day 1. Leveraging core concepts like KPI-driven development and micro-services they are able to successfully develop, deploy, scale and maintain data science models in production. 

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