635: The Perils of Manually Labeling Data for Machine Learning Models
Super Data Science: ML & AI Podcast with Jon Krohn
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Challenges and Solutions in Manual Data Labeling for Machine Learning Models
The chapter delves into the challenges faced by machine learning teams when manually labeling data for model development, emphasizing the importance of recognizing and addressing biases in labeled data. It discusses the societal, time, and capital costs associated with manual labeling, advocating for more sustainable and efficient automated processes. Machine teaching is introduced as an alternative approach, focusing on improving the effectiveness of conveying knowledge to models through techniques like weakly supervised learning and active learning.
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