To achieve the creation of genuinely dependable AI models, it is imperative to furnish algorithms with appropriately labeled data. This is precisely where the practice of data annotation assumes a pivotal role.

Data annotation is like adding a human touch to machine learning. Making datasets with correct and fair labels for machine learning is often overlooked but is super important for a machine learning application to work well.

What is data annotation?

Data annotation is the act of attaching helpful labels to data to make it easier for computers to grasp and make sense of. This data can take various forms, such as images, text, sound, or videos, and the labeling must be as precise as possible. The process of data annotation can be carried out either by humans manually or automatically through advanced machine learning tools and techniques.

The annotation processes involve choosing tools for different tasks. For example, we want to understand how people feel about a product by analyzing social media posts. We can quickly create a cool HTML form, deploy it in a snap, and invite people to rate posts as positive, negative, or neutral. It’s like creating our own sentiment radar!

Key steps in data annotation

Below we provide a brief explanation of the key steps in data annotation:

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