We all would have come across the word data annotation or data labeling when reading about machine learning and artificial intelligence. What is data annotation, and why is it imperative for machine learning models? This blog unveils everything you need to know about data annotation.
Data annotation
Data annotation is the process of identifying raw data and then adding labels to that data in the form of audio, video, text, images, etc., making it easier for machines to understand. Annotated data is crucial for supervised machine learning models because they need to clearly comprehend the input pattern to produce the desired output. But data annotation is, by far, time-consuming and requires manual effort. When precise training datasets are used, machines can easily detect objects without any ambiguity.
Do you think data annotation is different from data labeling? Not at all! They are the one and the same. Data annotation is also known by other names like data labeling, data tagging, data classification, etc. Labeled data finds applications in various fields. For example, let us take a self-driving car. Here, the training machine learning models make use of annotated video data. All the objects in the video are annotated to help ML models predict the movement of objects.
Significance of data annotation
The performance and precision of any supervised ML model depend on the quality of annotated data. In short, annotated data is the kernel of machine learning models. Data annotation finds various applications and is helping businesses and industries thrive and explore new possibilities. When developing machine learning models, the major challenge is to find accurately annotated data. With data labeling, the task is simplified. It lets machines understand real-world conditions and give accurate results.
Many companies are integrating AI technology with their workflow to automate their existing processes and to gain benefit from new opportunities. In AI or ML industry, having accurately labeled or annotated data gives you an extra edge when compared to your competitors. Data annotation is a surefire for a chatbot. It is one of the most prominent applications that make use of machine learning and AI. Chatbots are capable of enhancing user experience if powered by precisely labeled datasets.
Challenges of data labeling
Though data annotation can prove to be effective, it poses multiple challenges that one needs to overcome when building predictive models. The first and foremost challenge is the availability of time and resources. You might be aware that data labeling can be done both manually and automatically. Manual data annotation demands a lot of manual effort and, most importantly, time. Also, the quality of annotated data is always a question. It is common for humans to make mistakes. These errors impact the overall quality of the labeled dataset and thus affect the prediction of future events. To overcome these challenges, data annotators use advanced and smart tools that not only simplify the process but also give accurate results.
Conclusion
Data annotation is indispensable for artificial intelligence and machine learning industries. For an AI company to flourish, having precisely labeled datasets is very essential. Data labeling is a time-consuming and grueling task to achieve. With experts at your disposal, you can rest in peace knowing you receive only precise and quality results. If you wish to outsource your data labeling projects to an expert consultant, talk to us today. At Springbord, we have a pool of highly-skilled annotators who can understand your requirements and deliver exactly what you desire. Partner with us and see your business from a new dimension you have never experienced before!
