Data Annotation is a rigorous task of labeling data with metadata while preparing it to train a machine learning model. Data and metadata come in multiple forms, which include content types such as audio, text, images, or videos. These annotated datasets can be further used for training autonomous vehicles, chatbots, or translation systems. 

What is Data Annotation? 

It is typically a process of adding metadata to a dataset. This metadata usually takes the form of tags, which could be added to various data types like text, images, or videos. Hence, adding comprehensive as well as consistent tags is a part of developing training datasets for machine learning. 

Data annotation is a crucial stage of processing data, as supervised machine learning models allow you to learn and recognize multiple recurring patterns in annotated data. Once an algorithm has processed enough annotated data, it starts recognizing the same patterns when presented with new annotated data. So, as a result, data scientists must use clean annotated data to train machine learning models. 

Types of Data Annotations

There are variety of data annotations, and all suit distinct use cases. So, let’s run through the most common annotation types used for popular machine learning projects. 

Image and Video Annotation

Image annotation has multiple forms, such as bounding boxes or semantic segmentation. Bounding boxes are imaginary boxes drawn on images, and semantic segmentation is where every pixel in an image is assigned a meaning. This kind of labeling helps machine learning models perceive the annotated areas as a distinct type of object. 

Even video annotation involves adding bounding boxes, polygons, or key points to content. This is done on a frame-by-frame basis through a video annotation tool. 

Semantic Annotation 

It is the procedure of annotating various concepts within the text, such as objects, people, or company names. Machine learning models seamlessly use semantically annotated data to learn how to categorize new concepts in new texts. This also helps in improving the search relevance as well as training the chatbots. 

Text Categorization

Text or content categorization refers to the task of assigning predefined categories to the respective documents. For instance, you can tag sentences or paragraphs in a specific document by topic or organize news articles by subjects like national, sports, entertainment, or international affairs. 

Presently, how Brands are making the best use of Data Annotation? 

Building your own AI or ML model that acts and takes decisions like humans need loads of training data. So, for a model to take action and decide on its own, it must be trained to understand the information. This is why data annotation is a crucial undertaking for most businesses today. With human-powered high-quality annotations, companies could improve their AI implementations in a better way. Therefore, brands are securing enhanced customer experience solutions such as product recommendations, computer vision, speech recognition, relevant search engine results and more, from data annotation services.

Various companies use data annotation services to make the best use of it by combining a human-assisted approach with machine-learning. They also provide high-quality training data that offers you the confidence to deploy your AI & ML models at scale. So, whatever your data annotation needs are, data labeling platforms would efficiently understand and cater you with the market standard AI & ML project. 

Know-How Data Labeler could help you

Data Labeler offers high-quality labeled datasets which are accurate, convenient, expedited, and customized for AI and ML initiatives. 

We provide the best training datasets in the market, empowering you to increase your competitive advantage and growth. 

Hence, to summarize what do you get from Data Labeler.

– Highly Accurate Labeled Data

– Options on Real-time Labeling

– Guidance on Labeling Instruction and more

Contact us for detailed information – Sales@DataLabeler.com