Realities of ML Production
The conversation highlights the often-overlooked challenges in machine learning production, such as feature drift and organizational alignment. It emphasizes that while modeling and data exploration are enjoyable aspects, a significant portion of time is spent addressing software issues that can arise in real-world applications. Understanding these challenges is crucial for making a meaningful impact in the field.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
SDS 451: Translating PhD Research into ML Applications — with Dan Shiebler
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