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How to Choose a Data Annotation Tool?
So, you’re looking to start working on a new project or launch a new product that is built using an AI/ML model, and as you probably know already you will need data … a lot of it and now you quickly realize that not only finding high-quality training data but also data annotation will be some of the challenging aspects of your project. “The success of your AI and ML models is as good as the data you use to train them”. The precision you apply to data aggregation, labeling, and identification is, therefore quite important and shouldn’t be neglected!
Once you have the data, the most important question now is which data annotation tool should you use? or where do you go for the best data annotation and labeling services for AI and machine learning projects? This article is entirely about data annotation, and I will try to cover with it the following points: what the process is, why it is inevitable, the crucial factors that you should consider when starting the process of data annotation, and much more.
- Data Annotation: The Basics
- The Different Types of Data Annotation
- Tools and Techniques for Data Annotation
- Getting Started with Data Annotation
- The Importance of Data Annotation
The Basics!
Have you ever wondered what machine learning is and how data annotation plays a role in it? Data annotation involves marking or identifying components to support machine learning (e.g., just labeling images image 1 -> cat, image 2 -> dog, etc).
The basic principle of machine learning is that computer systems and programs improve their performance by seeing more and more concrete examples of what should be done in a way that resembles human cognitive processes (learning by experience), without direct human aid or intervention. They become self-learning machines that get better at their tasks with more practice, much like humans. Of course, this practice is achieved and considered good by analyzing and interpreting more (and better) training data.
