Why Data Annotation is Important for Machine Learning and AI?

Data annotation is the process of making the contents available in various formats like text, videos and images, recognizable to machines. Artificial Intelligence (AI) and Machine Learning (ML) companies are seeking such annotated data to train their ML algorithm learn the patterns and memorize the same for predictions.

Actually, to develop the AI or ML based models you need huge amount of data sets that is customized as per the model training requirements. Data annotation is one of them, helps machines to make various information understandable. Hence, here we will learn about what is data annotation, how it is done and why it is important for AI and ML.

What is Data annotation in AI or ML?

Data annotation is the process of labeling the contents recognizable to machines through computer vision or natural language processing (NLP) based AI or ML training available in various formats like text, videos and images.

It is simply the process of labeling or annotation making the object of interest detectable or recognizable while feeding into algorithms. And there are various techniques and types of data labeling done as per the requirements of the projects.

Text Annotation for Natural Language Processing in AI

Text annotation is simply done for NLP or speech recognition by machines to understand the communication process among humans speaking in their native languages. Text annotation is done for developing the virtual assistant devices and AI chat bots to give answers of various queries asked by the people in their speech style.

In text annotation, a metadata is also added to make the keywords recognizable to computers and utilize the same while taking crucial decisions. NLP annotation services is also doing the same job to annotate the texts using the right tools.

Video Annotation for High-quality Visualization Training 

Just like text annotation, video annotation is also done but here the motive is to make the moving objects recognizable to machines through computer vision. In video annotation, frame-by-frame objects are annotated precisely.

And video annotation service is basically used in creating the training data for visual perception model based self-driving cars or autonomous vehicles. And various types of objects are annotated in videos to estimate their movements.

Image Annotation for Object Detection and Recognition 

The most important and precious data annotation process to create the AI model. Actually, the main purpose of image annotation is, make the objects recognizable to visual perception based AI and ML models. And to get the quality training data sets you have to outsource the image annotation services to professionals.

Also Read: Five Reasons Why You Need To Outsource Your Data Annotation Project

In image annotation the object is annotated and tagged with special techniques making distinct type of object easily perceptible to AI-enabled machines. And there are different methods of image annotation to create the training data sets for AI companies.

Bounding box, semantic segmentation, 3D cuboid annotation, landmark annotation, polygon annotation and 3D point annotation are the leading methods used in image annotation as per the customize needs of the AI and ML projects.

NLP Annotation for Language and Speech Recognition

Just like text annotation, NLP annotation is done for speech recognition to make human language and communication process understandable to machines. Natural language process or NLP annotation is an art of labeling the sentences with metadata and annotated keywords to improve the natural language processing.

NLP annotation also used for sentiment analysis through machine leaning by classifying the texts while improving the sentence level-performance in the AI development. Actually, with right tagging, labeling and keynotes it becomes more clear and comprehensible to machines and improve the voice or text based communication.

Virtual Assistance and Chat bots are the real examples of AI-enabled applications learn from annotated texts and NLP annotation with right accuracy helps machine learning algorithms learn efficiently and effectively to give the accurate results.

Medical Annotation Services for Imaging Analysis 

Just like image annotation for text, video and images, medical images annotation is also done for creating a healthcare training data for machine learning and AI. Radiology images like X-rays, CT Scan, MRI and Ultrasound are the medical images annotated to train the models for diagnosing the various diseases automatically.

Medical image annotation job is done precisely by the experts radiologist doctors who manually annotate the each medical image using the right tool making the malady recognizable to AI machines detect similar indications in real-life use.

Summing-up

I think now you got know why data annotation is important for machine learning and AI projects. In fact, training data available in the form of annotated texts, images or videos is fuel to train the algorithms that can only create such autonomous models. You cannot imagine the AI and ML without sufficient training data set.

Also Read: How Much Training Data is Required for Machine Learning Algorithms?

And, there are different types of data annotation techniques to label different types of data as per the AI or ML project requirement and algorithm compatibility. And for each  type of annotation, a specialist is there to complete such tasks. And, human-powered annotated data sets is more important for right machine learning.

Also Read: Why Human Annotated Datasets is Important for Machine Learning?

Anolytics is one of the leading data annotation company, providing the image annotation services for computer vision training in AI and machine learning. It is also doing text annotation and medical image annotation using the right tools and techniques to make the objects in each image recognizable to machine accurately.

It is creating human-powered high-quality training data sets for wide range of sector like healthcare, retail, robotics and automotive making AI possible to integrate in such fields and provide a better living environment to individuals globally.