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KICK-OFF-SPRINT

Click on the different phases for more information.

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Goal

This Kick-Off-Sprint is not like an ordinary design sprint. This sprint not only gets to the bottom of important product problems, clarifies project issues and generates possible product solutions, creates and evaluates prototypes, but also addresses machine learning, its opportunities and limitations. Machine learning is brought closer to the client in order to generate possible solution proposals that include machine learning technology. When UX design and machine learning are combined can innovative products be created that stand out from the competition, are individually tailored to the user, and provide a personalized user experience. 

The goal of the Kick-Off-Sprint is to successfully bring machine learning and UX Design together in one sprint. 

Requirements

The condition for the Kick-Off-Sprint is that in the first meeting there is already a big possibility that the final product will contain machine learning technology. Therefore, it is important that an Machine Learning  Engineer is also present at the first meeting to get a better impression of the product vision. 

Duration

The Kick-Off Sprint consists of 4 phases and includes two and a half days with the client. The complete sprint takes about three weeks, depending on the availability of the participants. 

Benefits and Advantages 
  • UX Designers know opportunities and limitations of machine learning, can use it as design material and estimate it correctly - vice versa.

  • Better match between customer needs and machine learning algorithm - no misallocation of resources. The final product meets the customer's expectations.

  • Closer collaboration provides better understanding of each other's expertise. Which in turn strengthens interdisciplinary communication and ensures that concerns can be placed more easily within the team. 

  • Machine Learning Engineers can better apply their technical knowledge from the beginning.

  • Additional effort for expectation management in the project team can be saved. 

  • Better and easier interdisciplinary collaboration can be ensured.

 

Conlusion: A lot of time and effort and therefore resources and money can be saved. 

Share your experience with me

Since project «Nestor» is an ongoing process there are still many things that need improvement. Every feedback on the content, layout, or display of the information is useful. Questions of comprehension are also welcome or share your experience with me. Every constructive feedback helps me to make my practical master project better.

 

Your help and experience matters.

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