Options for Managing Data Annotation Workloads If you train these systems - or any other ML system with data that’s been labeled inaccurately, the results will be inaccurate, unreliable, and provide no value to the user. Medical images: Data engineers are training models to detect cancerous tissue or other abnormalities from X-ray, sonogram, or other medical images.Autonomous vehicles: Advancing self-driving vehicle technology is a prime example of why accurately training ML systems to recognize images and interpret situations are important.Language translation: ML models can learn to translate spoken or written words from one language to another.Optical character recognition (OCR): Data annotation allows data engineers to build training sets for OCR systems that can recognize and convert handwritten characters, PDFs, and images or words to text.Natural language processing (NLP): NLP systems can learn to understand the meaning of a query and generate intelligent responses.Chatbots: Data annotation can give chatbots the ability to respond appropriately to a query, whether spoken or typed.Text and internet search: By labeling concepts within text, ML models can learn to understand what people are searching for - not just word for word, but taking a person’s intent into account. Your ML model will only be as accurate as its training data’s annotation.Ĭonsider how the accuracy of data annotation could make or break these projects: Now, however, organizations are focusing their resources on data annotation to prepare data stacks for structured ML or training sets for unstructured ML.Īdding metadata to code is a relatively straightforward task, but there’s much more to consider when annotating data in preparation to train a machine learning or artificial intelligence system. Companies have used data annotation in the past to identify patterns and to make data searchable. What is Data Annotation?ĭata annotators create metadata in the form of code snippets that describe or categorize data. As a result, AI development teams are looking for ways to manage data annotation without sacrificing accuracy or quality. Compared to just a few years ago, data annotation has grown into a much larger, time-consuming task. With artificial intelligence (AI) and machine learning (ML) adoption on the rise, data annotation workloads are skyrocketing.
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