Wednesday, August 18, 2021
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.