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.
The Need for Annotation in Machine Learning Any AI/ML project cannot be successful without access to the right training datasets. Continually feeding algorithms with the high-quality and precise data with a learnable ‘signal’ will bring in gradual improvement, and that is the true power of data annotation. Besides, annotation In Machine Learning provides the initial setup that is required to feed the algorithms, based on which they can deliver accurate outcomes. The more annotated data is used to train the AI/ML models, the smarter it gets over time. Bottom Line Annotation in machine learning is important to train the models and get desired outcomes. These annotated datasets enhance the decision-making abilities of the NLP and computervision based models and enable them to discriminate between various inputs and yield accurate outputs. Besides, this is what empowers them to predict patterns that humans can’t.