Accelerating Smart Models with Annotation in Machine Learning Artificial Intelligence and Machine Learning rely heavily on data. They identify the patterns that humans cannot find. But, training these computer-vision based models requires consistent streams of high-quality and precise data. Hence, the key to building a precise AI Ml algorithm is getting the ‘smart data’. After all, AI is only smart if the data fed is smarter. However, collecting the raw data and processing it is not enough. This data must be structured to be fed into Machine Learning algorithms because otherwise, it is just noise for a supervised model. Hence, this leads to the need for annotation in Machine Learning. To put it simply, data annotation is an effective process to train the AI and ML projects.
What is Data Annotation? Data annotation is the process of adding tags and meta tags to the data to train the Artificial Intelligence and Machine Learningmodels. The object could be in the form of text, image, video, or any other form of content. Essentially, annotation depends on the project requirements such as semantic segmentation, lines and spines, 2D/3D bounding, image annotation, video annotation, content moderation, text categorization, etc. Adding tags and labels makes the computation of necessary attributes easier for these models.