Data Annotation is typically the term used to define the process of data labeling in multiple data formats such as images, video, text, to train machines for understanding the data and completing specific tasks. The more popular type of Machine Learning, which is supervised Machine Learning, deals with large data sets with annotated data used to train AI models by perceiving and learning the data patterns that the annotations represent. Machine Learning models. Deep Learning algorithms are used to The annotated data helps ML and AI models understand data patterns and produce the desired outcomes.
Data Annotation Solving Real-Life Problems
Data annotation solves issues like classification, regression, and more. An example for classification issues can be a doctor trying to predict whether the patient is suffering from a specific disease, by using systems that assign the patient’s health data to reach a conclusion with every data element- ‘disease’ or ‘no disease’. Regression issues include situations where the system is designed to establish a connection between the dependent and independent variables in question, like for example, the dependent variable could be the advertising budget of a business, while the independent variable can be the sales of a product.
Benefits of Data Annotation
Improved & Accurate Results
Data annotation helps train the Machine Learning algorithm to become familiar with different factors that helps the model to use its data pool to generate the most relevant and accurate results in any given situation. Data Annotation ensures quality, scale, security, and speed when working with machines and models.
Better User Experience
Machine Learning systems powered by AI models are designed to build applications that deliver a seamless experience for the end users. Virtual assistants and chatbots have been designed to cater to user needs and solve any queries users may have. Major Search Engines are using data annotation and Machine Learning to generate relevant results to user searches, by tracking past behavior of the user. All of this boils down to data annotation helping improve user experience.
Lesser Burden on Human
Data Annotation has also drastically reduced and controlled the need for human intervention in most tasks. The results generated by the systems are definitely supervised by humans to determine applicability and accuracy. But the groundwork of labeling and annotating data is automated which reduces burden on humans.
Powering Advanced Technologies
Automated systems and smart systems are majorly built on advanced technologies like Deep Learning, Machine Learning, Natural Language Processing, Artificial Intelligence, and more. These technologies and systems constantly need to be fed data that can help these systems learn, evolve, and complete the assigned tasks. All of the models powered with these technologies need organized data which is not possible without data annotation. Data annotation forms the base for the advanced systems to learn and predict.
Data Annotation is the process that organizes raw data. When the AI and ML models are trained with annotated data, they’re able to track data patterns and derive near-to-accurate conclusions about risks and possibilities. When raw data is transformed into structured data and further used to make predictions, decision-making for stakeholders becomes easier, because all the predictions are derived from real-time data. It’s needless to say, data-backed decisions are always the best and the most fruitful ones in the long run!
Benefits of Data Annotation in Machine Learning
> ML models are trained with supervised learning to ensure accurate predictions, which is made possible on by annotated data
> ML automated systems ensure a seamless user experience by implementing data annotation
> Search Engines using ML technology are implementing data annotation to track user behavior and display relevant & accurate results
> Data annotation is helping Machine Learning models enable speech recognition with Natural Language Processing
> Data annotation is helping AI models reach their fullest potential
Why Does Data Annotation Matter?
Data Annotation stands as the backbone of running supervised learning models successfully. The accuracy and quality of results delivered by these models largely depend on how much data is annotated, and how well the data is annotated.
Machine Learning models come with a wide range of complex applications in multiple sectors. To ace such complex applications, Machine Learning models need to find and be trained with high-quality annotated data, and data annotation caters to that need.
Without data annotation, a machine would find any piece of data, in any format, be it video, image, or text, as identical. A machine does not come with an inherent understanding of how the world works. Instead, machines need to be trained to help them develop that understanding. Data annotation is what helps the algorithms to identify specific data elements, understand what each data element is all about, and segment the data elements into comprehensive categories to train computer vision and machine learning models.
Fact is, we cannot imagine AI-powered or ML-powered models, projects, solutions, and applications without high-quality and organized data sets at hand. Annotated data, in multiple formats such as image, audio, video, and text, is the fuel that enables Machine Learning models and AI models make predictions and provide solutions.Image courtesy: Photo by Andrea De Santis on Unsplash