Artificial intelligence (AI) companies spend a lot of time building and refining their training datasets for machine learning (ML) projects.
The reason? For any ML platform to perform to an optimal standard, it is important to have high-quality training datasets.
The subsequent question could be: How can a training dataset’s quality be determined? The answer is data annotation.
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Whether it is search engine results, product recommendations, autonomous drones or self-driving cars, data annotation provides the foundation for building and enhancing ML applications across sectors and domains.
What is data annotation?
Let us start with the fundamentals. For any machine learning model to understand what it needs to look for in cluttered, real-world surroundings, it must learn from experience. This essentially comes as training data.
Training an ML model to understand its environment and make necessary decisions followed by appropriate action requires large volumes of quality training data.
Data annotation is the process of recording, tagging and labeling the key features of data that ML systems must be able to identify on their own.
Data comes in different formats such as images, text, video and more. For the purpose of supervised machine learning, labeled datasets are essential to enable machines to understand input sequences clearly.
Data annotation needs time. But it is a key component that helps machine learning projects function as intended.
Training machine learning models to predict and generate the expected outputs is the role of data annotators.
Benefits of data annotation
It is clear by now that data annotation helps ML models to be trained for accurate prediction with supervised learning. But its benefits go far beyond that.
Data annotation plays a key role in the world of automation, too.
Automated ML-based systems and educated ML algorithms provide a streamlined end-user experience.
Chatbots and other digital assistant systems generate appropriate answers to end-user questions on demand.
ML technology is another key component of search engines that generates millions (and more) of results relevant to a search performed by a user. Machine learning has helped improve the accuracy of search engine results by taking into account past search behavior.
Speech recognition is being leveraged by virtual assistants to understand and interpret human language and ways of communication with the help of natural language processing (NLP), a branch of artificial intelligence.
It is essential to employ the right methods to annotate data to train computer vision-based ML models.
Several types of data annotation techniques and steps are involving in creating the requisite datasets. We will go through each step one after another.
For speech recognition by machines or natural language processing, text annotation helps provide a mechanism for communication between humans conversing in their own languages.
Text annotation is used in the development of chatbots and virtual assistant devices that respond to user queries, thus helping organizations streamline user experience, reducing the stress on customer support and improving process efficiency.
Metadata too is introduced in text annotation tools for machine learning to create keywords that can be identified by search engines.
The keywords are also used in decision-making for future searches. NLP-based annotation models perform the same process by leveraging the essential tools to compile texts.
Image annotation for high-quality visualizations
Video annotation is performed similar to text annotation with the objective being that moving vehicles through computer vision (CV) must be made identifiable to machines.
Video annotation enables frame-by-frame objects to be annotated accurately. Video annotation service is also mainly used to create training data used by self-driving and autonomous cars based on visual perception models.
Image annotation for object detection
It is one of the most important data annotation methods to build artificial intelligence models.
The key objective is to render objects that are recognizable by machine learning-based models using visual interpretation.
In image annotation, images are labeled and tagged with extra elements that help AI-enabled models to identify different kinds of objects.
To develop training datasets to power automation, many types of image annotation strategies are available.
However, the main methods used for image annotation based on the unique requirements of machine learning projects are geometrical annotation, landmarks annotation, textual segmentation, rectangular box, three-dimensional data annotation, and three-dimensional cylindrical shape annotation.
Supervised versus unsupervised machine learning
The difference between supervised and unsupervised machine learning lies in the training dataset.
In supervised machine learning, the training data is labeled to enable the system to understand and fulfill the requirement.
For example, one objective of the program is to identify the animals in the images. It already has tons of images with the label “animals”. But in the case of supervised machine learning, it uses references to compare the fresh data to be able to generate the observations.
In unsupervised machine learning, the system does not have any identifiers. Therefore, the framework makes use of characteristics and multiple other methods to classify the different objects. Engineers can train the software to recognize the visual characteristics of animals, such as their paws, tails and other features, but the task is much more complex than in supervised machine learning, where such signs play a key role.
Data annotation is an essential part of AI and machine learning, which offer tremendous value across business processes and to our everyday lives.
We could even go a step far and say that without data annotation, machine learning models would be almost non-existent. Because the only way the AI for image detection would be able to identify a human face in an image is if many images with the label “face” are already available.
The global data annotation tools market is forecast to reach USD 3.4 billion by 2028, growing at a CAGR of 27.1 percent in the seven-year period from 2021. It is an indication of the growing significance of data annotation as increasingly nuanced datasets become essential to build out more nuanced issues of ML.