With the acceleration of artificial intelligence deployment in self-driving cars, intelligent security, smart healthcare, and other fields have become popular. We can see that this will be the era of artificial intelligence in the near future.
As the foundation of the AI Industry, Data Annotation is laying the foundation for the emergence of the AI era. Data Annotation is one of the most time-consuming and labor-intensive processes in AI/ML projects. Although this task is simple, it plays an important role, accounting for 60 – 70% of the time and workload in an AI model project. Besides, the Data Annotation workforce is also one of the most limitations in deploying the AI model for organizations.
If your company is developing an internal AI project with a small amount of data trained, needs less than 10 experts, managing the data annotation team is relatively simple. You have many options such as using temporary staff, crowdsource/freelancer. Although there are differences in cost, concentration ability and effectiveness, the differences will not be too much. You can freely choose an option as long as you feel comfortable and suitable for the organization’s working style.
However, if you need a large amount of data trained like self-driving car company, AI data solution companies with a workforce from ten to hundreds of employees, controlling a flexible Data Annotation team with maximum capacity is a difficulty that needs to be solved and continuously optimized.
Difficulties and disadvantages that organizations may encounter in the process of training AI model:
- Productivity and consistency
- Recruitment time
- Investment and salaries cost
- Distributed concentration away from core business
- Business continuity plan
These 5 points are the main difficulties of the Data Annotation process. As a result, Outsourcing Data Annotation services are increasingly developing as a lifeline for the AI data solution industry. According to Grand View Research, the global market scale for Data Annotation tools is expected to increase at a CAGR of 26.6% from 2022 to 2030.
It is clear that manual Data Annotation maintains high accuracy among different approaches and accounts for more than 81% of the global revenue market share. Therefore, instead of using in-house talent resources for the Data Annotation process, AI model companies now tend to outsource Data Annotation services from third parties to handle the company’s pain points.
Beework.ai confidently asserts that we can undertake whatever scale projects and satisfy the core requirements of an Ai model with more than 4 years of industry experience. If you want your AI model to deliver accurate results, get in touch with us to discuss your ideas and requirements.