Recommended outsourcing service for streamlining annotations! What are the points to consider when comparing companies?

With the advancement of AI, the areas where AI is utilized are expanding more and more. In order for AI to correctly recognize data based on its purpose, learning is necessary. There are various methods of learning, such as "supervised learning," "unsupervised learning," and "reinforcement learning." In "supervised learning," annotation work is done to create training data. Annotation requires a large amount of training data to be created by human hands, and it can become a bottleneck in terms of time and cost during AI development. If it is difficult to do annotation work in-house, one option is to use an annotation outsourcing service to improve efficiency. In this article, we will explain the points to consider when choosing an outsourcing service company.

Table of Contents

1. What is Data Annotation?
2. Reasons for the Increase in Data Annotation Demand
3. Common Challenges of Data Annotation
3-1. Time-consuming and labor-intensive
3-2. Quality management and progress management are essential.
3-3. Securing human resources and improving efficiency takes time and effort.
4. Benefits of Using Data Annotation Services
4-1. Be able to focus on development tasks
4-2. Can reduce labor costs
4-3. Can ensure the quality of teacher data
5. Points to consider when choosing a data annotation service
5-1. Achievements
5-2. Quality
5-3. Areas of Expertise and Strengths
5-4. Progress Confirmation and Sharing
5-5. Cost
5-6. Security
6. Summary
7. Data Annotation Services by Human Science Co., Ltd.

1. What is Data Annotation?

Data annotation is the process of adding information to the "object to be recognized" in data such as text, voice, images, and videos, in order to create training data for AI development. This process of creating training data is called annotation. The training data is used for AI learning.

2. Reasons for the Increase in Data Annotation Demand

With the advancement of AI learning technologies such as deep learning, environments have been established to accurately train various data such as big data. As the range of AI utilization expands, the need for annotated training data is also increasing. This can be seen as one of the reasons for the increasing demand for data annotation.

3. Common Challenges of Data Annotation

When performing data annotation in-house, it can potentially add burden to the original AI development tasks and hinder the progress of a smooth AI development project. Here, we will look at three factors that contribute to this.

3-1. Time-consuming and labor-intensive

No matter what type of annotation, the data annotator manually adds information (tags) to each piece of data. There may be a need to annotate a large amount of data, ranging from thousands to tens of thousands, and it may take several weeks to several months to complete all the data. Annotators are required to have the patience to continue monotonous and tedious work for a long time, as well as the ability to understand and accurately perform tasks such as work instructions and specifications. Programming skills and knowledge of AI are not required, but this is not a task that anyone can easily do. Depending on the difficulty of the annotation, there may be a training period for work proficiency before actual work. As such, annotation requires both a huge amount of work time and suitable resources.

3-2. Quality management and progress management are essential.

If there are discrepancies in recognition or mistakes in annotations by data annotators, the quality of the training data will not meet the standards and the accuracy of AI recognition will decrease. It is necessary to manage the quality by checking if the work instructions and specifications are accurately reflected in the work data. Data annotation work takes time and in some cases involves handling large amounts of data and people, so proper progress management is essential. If there is a delay, it will naturally affect the schedule of AI development.

3-3. Securing human resources and improving efficiency takes time and effort.

In this way, managing quality and progress without compromising quality in the annotation process, which requires a huge amount of time and manpower, is very time-consuming. Even if you try to gather appropriate resources, it is often difficult to make progress in hiring, and it also takes time and cost to have personnel in-house. In addition, in order to streamline the process, using tools with automatic annotation and progress management functions can achieve a certain level of efficiency, but it is still unavoidable that a lot of time is spent on management and annotation work.

4. Benefits of Using Data Annotation Services

One of the benefits of using annotation services for AI development companies is the ability to outsource the massive amount of annotation work to external sources, allowing them to focus on their core AI development tasks. This also provides the advantage of reducing costs for data annotators and management expenses.

4-1. Be able to focus on development tasks

AI engineers will no longer be burdened with data annotation tasks and will be able to focus on their core development work.

4-2. Can reduce labor costs

By securing dedicated personnel for data annotation in-house, in addition to the cost of personnel expenses, there will also be management costs for human resources such as personnel and labor. Furthermore, even if personnel are secured, data annotation work may not always be required. If there is a vacancy in operation, depending on the employment status, unnecessary costs may be incurred. By outsourcing to a service provider, it is possible to secure data annotators according to the necessary period, thus reducing personnel expenses and waste.

4-3. Can ensure the quality of teacher data

Data annotation is a labor-intensive task that requires patience. It may seem simple, but it actually requires a lot of experience. In order to ensure quality, it is necessary to properly manage the work of data annotators, check the data, and develop human resources. This also requires specific knowledge and experience in data annotation. By outsourcing the work to a service company with a wealth of data annotation experience, you can obtain more stable and high-quality training data.

5. Points to consider when choosing a data annotation service

When choosing a data annotation service, it is important to select a company that aligns with the purpose of AI development, various requirements of the company, and the way of working. Here, we will explain the key points to consider when choosing a company, such as whether they have experience in data annotation and if they can meet the desired quality.

5-1. Achievements

Data annotation is performed on various types of data such as images, text, videos, and audio. Some outsourcing services may specialize in specific types of annotation, such as image annotation, so be sure to check their track record for annotation that aligns with your company's goals. With a wealth of experience in this field, they should be able to accommodate your requests for annotation.

5-2. Quality

Ensuring the quality of data annotation is crucial. It greatly affects the subsequent AI development process whether or not data can be created according to the work instructions and specifications. To avoid the situation where the desired quality of training data is not delivered after requesting the work, it is reassuring to have the ability to conduct trials and partial deliveries for quality assurance. Also, let's check what kind of system and structure are in place to ensure quality, such as the method of checking data and the development and thoroughness of information related to specifications.

5-3. Areas of Expertise and Strengths

Data annotation covers various types of data such as images, videos, audio, and text. Some outsourcing companies may have experience in image annotation but not in text annotation.
In addition, there may be cases where high difficulty annotation experience is required for certain fields even in image annotation. For example, in medical image annotation, the presence or absence of such annotation experience can greatly affect quality and productivity. It is important to confirm whether the outsourcing company's expertise and strengths match the annotation requirements required by the company.

5-4. Progress Confirmation and Sharing

I am worried if I don't know the situation from the start of the work to the delivery. It is also a point of selection whether you can check and share the progress status. Are you able to handle partial deliveries during the work? It is also important to check if you are using a tool that allows you to check the progress status online and directly access and understand the situation.

5-5. Cost

The cost of data annotation services varies depending on the outsourcing company. Request a quote to compare how much you can save compared to performing annotation in-house. Also, be sure to gather quotes from multiple companies with the same criteria for checking, and compare them between outsourcing service companies.

5-6. Security

Let's confirm if we can handle highly secure data. Depending on the security level, let's check if we can work not only remotely, but also in a security room, at the client's site, or on-site. Also, it is ideal for a company to have a multi-faceted security measures, such as a well-established information security and personal information management system, and providing security education to workers.

6. Summary

Data annotation is a necessary process in AI development, but when performing annotation in-house, engineers must also handle tasks other than development in order to secure resources and ensure quality and delivery time. By choosing a data annotation outsourcing service, engineers can focus on their main tasks and achieve better results in AI development projects.

7. Data Annotation Services by Human Science Co., Ltd.

Rich track record of creating 48 million pieces of teacher data

At Human Science, we are involved in AI model development projects in various industries such as natural language processing, medical support, automotive, IT, manufacturing, and construction. Through direct transactions with numerous companies including GAFAM, we have provided over 48 million high-quality training data. We handle various annotation projects regardless of industry, from small-scale projects to large-scale projects with 150 annotators. If your company is interested in introducing AI models but unsure of where to start, please consult with us.

Resource Management without Using Crowdsourcing

At Human Science, we do not use crowdsourcing and instead directly contract with workers to manage projects. We carefully assess each member's practical experience and evaluations from previous projects to form a team that can perform to the best of their abilities.

Utilize the latest data annotation tools

One of the annotation tools introduced by Human Science, AnnoFab, allows customers to check progress and provide feedback on the cloud even during project execution. By not allowing work data to be saved on local machines, we also consider security.

Equipped with a security room within the company

At Human Science, we have a security room that meets the ISMS standards in our Shinjuku office. This allows us to provide on-site support for highly confidential projects and ensure security. We consider confidentiality to be extremely important for all projects at our company. We continuously provide security education to our staff and pay close attention to the handling of information and data, even for remote projects.