Reproducibility Challenges
The conversation highlights the ongoing struggle with reproducibility in data science, emphasizing the importance of tools like git and automated testing. Both Jon and Martin discuss the challenges posed by Jupyter notebooks and the need for a supportive R&D culture within organizations. Without a focus on collaboration and clear processes, achieving meaningful results in data projects remains elusive.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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