Data annotation is the process of labelling and categorising data aimed at preparing it for Artificial Intelligence (AI) model training. The goal of data annotation is to deliver the labelled data set, which can be used by the AI model to learn the relationships between the input data and the desired result.
Data annotation can be done by humans or with automated tools, but human annotation is often preferred by clients because it is more accurate and provides a higher-quality data set. This process may be time-consuming and resource-demanding, however it is a key factor in the development of an effective AI model.
The first step in the data annotation process is defining a task that has to be carried out. This can be anything, from labeling objects in an image to text categorisation according to the defined class.
The next step is to prepare data for annotation. It may include cutting images, decreasing the size of audio files or cleaning/normalising the text data.
Then, the data is labeled. This is usually done through manual labelling, however there are tools and platforms that make it possible to automatically label the data. It is important to carefully label the data, as errors made at this stage may lead to data misinterpretation by the AI, and therefore, result in providing incorrect answers.
The labelled data is then verified in terms of precision. This step is important, as it allows you to make sure that everything is accurate and consistent.
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data annotation
The quality of annotated data has a direct impact on the performance of the AI model. If the data is mislabelled, the model may struggle to learn effectively, which can result in incorrect predictions. That is why it is important to carefully choose the methods and tools that will be used for data annotation and check the labelled data before it is implemented for training.
We have a great deal of experience with tasks related to data annotation. Our experts carry them out remotely or at the client’s premises – in any country!
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