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In the world of artificial intelligence (AI) and machine learning (ML), data is king. But raw data on its own has little utility until it has been accurately labeled and processed, making data labeling a crucial step in the AI model training process. For full stack web developers and system administrators delving into AI, understanding how to automate data labeling efficiently can fast-track the development of robust AI applications. Data labeling involves tagging data with one or more labels that identify its features or what it represents. In context, this could mean marking an image with the object names it contains, annotating texts based on sentiment, or categorizing audio files.