Based on Gartner's research, 85% of AI projects fail. In this talk, we show the most typical mistakes made by the managers, developers, and data scientists that might make the product fail. We base on ten case studies of products that failed and explain the reasons for each fail. On the other hand, we show how to avoid such mistakes by introducing a few lifecycle changes that make an AI product more probable to success.
DeveloperWeek Europe 2021
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KEYNOTE: Codete -- Avoid Mistakes Building AI Products
Obtained a PhD degree in Computer Science in 2015 at the Jagiellonian University in Cracow. CTO and founder of Codete. Leading and mentoring teams of Codete. Working with Fortune500 companies on data science projects. Built a research lab that is working on machine learning methods and big data solutions in Codete. Give speeches and trainings in German and English in data science with a focus on applied machine learning. Currently involved in trainings at O’Reilly.
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