![]() ![]() ![]() The best way to achieve this is by giving the image some sort of description, otherwise referred to as annotation. You can refer to image annotation as the process of making an image easier to find. Whether you are new to data annotation or a seasoned professional, this article will provide valuable insights into the world of data annotation and help you stay on top of its latest trends.īy the end of this article, you'll learn aboutīreakdown of image annotation and its importance With the growing value of AI and machine learning and the exponentially growing amounts of data in the world, data annotation has become even more essential for businesses and organizations to stay competitive. We can make a bold statement and call data annotation an ingredient in the data processing cycle one can't afford to avoid. – thus making the role of data annotation exceptionally important. ![]() Note that unstructured data makes up a big portion of data in the world – like emails, social media posts, image and audio data, text, sensor data, etc. Without it, machine learning algorithms would be lost in a sea of unstructured data, struggling to distinguish one piece of information from another. During the last 5-10 years, data annotation became more critical for machine learning systems so they can work effectively. Previously, data annotation was not as crucial as it is now for the reason that data scientists were using structured data which did not require many annotations. Through this article, we'll examine what carries the core responsibility for ready-to-train data, also known as data annotation.ĭata annotation is the action of adding meaningful and informative tags to a dataset, making it easier for machine learning algorithms to understand and process the data. A machine learning model's life starts with data and ends with the deployed model, and turns out that high-quality training data is the backbone of a well-performing model. When it comes to the global trend nowadays - artificial intelligence and machine learning, the first thing we care about is data. ![]()
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