👉 Annotation engineering is the process of designing, creating, and maintaining the guidelines, tools, and workflows necessary to ensure consistent and high-quality data labeling for machine learning models. It involves defining the structure, format, and criteria for annotating datasets, which can include specifying how to label different types of data (like images, text, or audio), establishing annotation guidelines to minimize ambiguity, and developing the tools and infrastructure needed for annotators to work efficiently. This process is crucial for producing reliable labeled data, which directly impacts the performance and accuracy of machine learning models. Effective annotation engineering also includes continuous evaluation and improvement of labeling practices to adapt to evolving model requirements and data complexities.
