For example, training a model classifying cats and dogs requires an image dataset containing explicit “cat” and “dog” labels. There are cases when annotation is the only way to record target labels or features. Producing tools for data annotation is a growing industry forecasted to surpass $13 billion in market size by 2030.Īn example of data annotation in NLP: Names and phrases labeled based on their meaning ( Source)ĭepending on the context, people also refer to this activity as “tagging,” “categorizing,” or “transcribing.” However, in this context, all these terms mean that the annotation extends the data with information used in the modeling process.īelow are the main use-cases of data annotation: Many AI tools and services of big tech companies like Microsoft’s Bing Search or Facebook rely on human-annotated datasets. In other use-cases, we can use data annotation to decide about uncertain decide about uncertain predictions (with probabilities close to ) or validate our model.ĭata annotation is a widely used practice. This makes model training possible, improves data quality, or improves model performance. In data annotation, we label or relabel our data with the help of human annotators supported by annotation tools and algorithms. To acquire labels or improve labeling quality, we can conduct data annotation. Labels can be noisy, limited, and biased (e.g., user-added tags and categories) or entirely missing (e.g., object detection). However, we do not always have labels in our data or with the required quality. For example, when predicting stock prices from past values, price acts both as a target label and an input feature. Often, the data we work with already comes with good quality labels. This dependence highlights the importance of datasets in Machine Learning and the methods by which we collect them. Machine Learning increasingly becomes a key component in businesses’ everyday offerings and operations, and the performance of these models depends on the quality of data they work with.
DATA ANNOTATE HOW TO
Do you wonder what data annotation is and what it is good for? Do you consider using annotated datasets for your AI and Machine Learning projects? Would you like to learn more about data annotation, the benefits it can bring to your project, and how to integrate it into your Machine Learning workflow?
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