This ebook focuses on the role of automated and semi-automated data labeling in accelerating machine learning projects. It explains why labeled data is critical for training high-performing models and outlines the limitations of fully manual annotation. The guide introduces AI-assisted labeling workflows that combine pre-trained models, active learning, and human review to improve speed and accuracy. It covers best practices for quality control, bias reduction, and scaling annotation efforts across large datasets. Readers gain insight into how automated labeling reduces costs while maintaining the precision required for enterprise-grade AI systems.

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