Data annotation is an essential part of Machine Learning and Artificial Intelligence development because they direct and educate the computer to think similar to human beings. The expansion of smart devices is a huge representative model of what successfully annotated and processed data can initiate and produce.
Different types of data annotations are available on the market for dominant types of the data, such as:
Image annotations– Technology used for multiple image processing
Audio and text annotations– Commonly used for translations, summarization of the long text, and similar tasks.
Video annotation– Used for recognizing and tracking the objects in a video through every sequence of it.
As you can see, the benefits of data annotation are numerous for every process included in Artificial Intelligence and Machine Learning development processes, and it is an integral part of digital transformation that undoubtedly changes the way we think and function on a daily basis. Actually, data annotation is one of the main pillars of digital transformation, because data outcomes and their capabilities for processing are highly dependable on the quality of data annotating, regardless of data type or purpose. It can be a time-consuming process and if not handled properly it can also result in a money waste. Companies are mostly willing to develop Artificial Intelligence and Machine Learning applications that can benefit from, but they are also aware of the risk mentioned above, so they hire a professional company to take care of and create a high-level service-related product.