Data Science Failures
Discover the ten reasons why data science projects often fail, including issues like data readiness and model complexity. There's a pressing need for open-source AI development and for practitioners to have a say in policy discussions. Emphasizing the importance of rigorous scientific methods, the conversation highlights how interdisciplinary knowledge across various fields is crucial for advancing AI effectively.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
833: The 10 Reasons AI Projects Fail — with Dr. Martin Goodson
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