Artificial intelligence and machine learning are the two emerging technologies that bring excellence in innovation across various sectors. But to create such systems, plenty of training data is required to identify things and relevant solutions, which further needs to be annotated by human resources to prepare data.
Data annotation plays a significant role in AI and ML projects in any industry. While it is not easy to turn raw data into comprehensive data, there is one process that adds a bit of information to raw data – giving relevant structure to data, which is otherwise useless to supervised learning algorithm and this is what data annotation means.
What is Data Annotation?
Data annotation is a practice to ensure AI and ML projects are well-versed with the right information and skill. It serves as an initial setup for any ML model to make it easy to understand and discriminate against various inputs. By feeding tagged and annotated datasets via the algorithm, you can develop a model that can become smart over time.
You are required to determine and annotate particular data so machines can categorize information relevantly. AI cannot succeed without access to the right information and feeding it in the right manner with learnable signal bring consistent improvement and that’s the strength of data annotation.
What do we need to annotate?
There are various sorts of annotations, depending on what form of data it is. It ranges from an image to video, text categorization, content, and semantic annotation. Identify what you exactly need to be successful. Think about your specific business goals and what kind of data help accelerate your project.
Importance of Quality and Accuracy in Data Annotation
Accuracy of data annotation will play a crucial role in whether or not the system will work properly and further identify items and other outcomes. If you’re not satisfied with the quality of your translation and datasets, you can closely work with an agency that could handle localization and data annotation within a reliable platform.
The reason data annotation is important is because even the smallest error or mistake could result in adevastating effect. If you’re working with an unsupervised ML project, sooner or later, you will need to get a smart data annotation system to reach a superior performance of algorithms.
How much data do you require for AI/ML project?
The short answer is as much data as possible. But keep in mind data need to be gathered based on specific parameters such as needs, domain, and interests. It is great to have data annotation system solutions to handle large datasets and evaluate accuracy to create the single source of truth used to train algorithms.
What are the types of Data Annotation?
- Text annotation – to train machines to comprehend the text. Types include semantic annotation, intent annotation, and sentiment annotation
- Text categorization – allocate categories to the sentences in the document or paragraph in relevance to the subject
- Image annotation – labelling images to train AI/ML model and gain an optimal level of comprehension. Types include image classification, object recognition, and segmentation
Data annotation enables AI to reach its full potential. With a plethora of benefits you acquire from AI-powered projects, you will stay rest assured knowing data is annotated correctly and make the most out of it.
