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11min chapter
Open source data labeling tools
Practical AI: Machine Learning, Data Science, LLM
Challenges and Strategies in Data Labeling
This chapter explores the challenges of labeling large datasets, including the time-consuming nature, ensuring labeling quality, and dealing with biases. It discusses the importance of investing in labeling tools, verifying translations, and the limitations of crowd-sourcing for certain data types. The chapter also covers strategies for quality control in data labeling, the concept of 'model in the loop' labeling, and the use of tools like Label Studio for enhancing data science team productivity.
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