Avoid Mistakes Building AI Products


Karol Przystalski
Codete, CTO

Obtained a PhD degree in Computer Science in 2015 at the Jagiellonian University in Cracow. CTO and founder of Codete. Leading and mentoring teams at Codete. Working with Fortune 500 companies on data science projects. Built a research lab for machine learning methods and big data solutions at Codete. Gives speeches and trainings in data science with a focus on applied machine learning in German, Polish, and English. Used to be an O’Reilly trainer.


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 succeed.