AI-driven, powered by AI, transforming with AI/ML, etc., are some taglines we have heard far too often from the products we are being sold every day. Everyone is chasing after the promised land of machine learning but so few fully understand it. And that is indeed the origin of most of the challenges that data scientists face today in the execution of their ML projects.
Eliezer Shlomo Yudkowsky, an American artificial intelligence researcher, says —
“By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.”
In this blog, we will look into some of the complaints and misunderstandings that turn into these challenges when not addressed head-on from the beginning of the project.

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10 Key Challenges Data Scientists Face In Machine Learning Projects
1.05 GEEK