
Today, AI is rapidly expanding and being applied in various fields. However, creating the "training data" necessary for AI development and machine learning often requires annotation work. Annotation involves tagging large amounts of data, which can sometimes take weeks to months. When conducting this work in-house, it can incur substantial costs, including resource allocation, quality management, and progress management. In this article, we will discuss the benefits of outsourcing annotation work and outline seven key points to consider when doing so.
- Table of Contents
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- 1. Advantages and Disadvantages of In-House Annotation
- 2. Benefits of Outsourcing Annotations
- 2-1. Cost Reduction and Time Savings
- 2-2. Reducing Management Burden
- 3-3. Improvement and Stabilization of Quality
- 3. 7 Tips for Choosing an Annotation Outsourcing Partner
- 3-1. Is it suitable for our company's requirements?
- 3-2. Expertise and Experience
- 3-3. Quality Control
- 3-4. Security
- 3-5. Scalability
- 3-6. Communication
- 3-7. Price and Pricing Conditions
- 4. Achievements in Human Science
- 4-1. Project Overview
- 4-2. Issues and Proposals
- 4-3. Effects After Implementation / Customer Feedback
- 5. Human Science Annotation, LLM RAG Data Structuring Agency Service
1. Advantages and Disadvantages of In-House Annotation
Here, we will look at the advantages and disadvantages of in-house annotation.
Benefits
When in-house production is implemented, there is less risk of data leakage to external parties, making it relatively safe from the perspective of security and privacy protection. Additionally, the annotation process can be designed flexibly, allowing for quick responses to changes in annotation requirements or specifications, such as informing annotators. Furthermore, it is possible to monitor quality and productivity in real-time. If resources can be allocated to manage annotation and human resources, the benefits gained from in-house production will be significant.
Disadvantages
When AI engineers perform annotations themselves, it not only causes delays in AI development but also leads to increased development costs, as it requires high-cost engineers to carry out the annotations. On the other hand, even if specialized personnel resources for annotation can be secured, if the annotation work is not continuous, there will be idle time, resulting in waste, and managing and controlling these personnel resources incurs significant costs.
Furthermore, to ensure the quality and productivity of annotations using a large number of personnel resources, experience and skills in annotation management, which differ from development, are required. In the case of in-house production, the reality is that if such skills are not possessed, ensuring quality and productivity is more difficult than expected. In fact, we often hear from our clients that "although we tried to handle annotations in-house, it did not go well, and the lack of these skills and know-how has become an internal issue, leading us to consult your company."
2. Benefits of Outsourcing Annotations
While this slightly overlaps with what was mentioned above, the annotation work itself does not require much specialization. However, to perform annotations properly, expertise in management related to annotation-specific personnel and tasks is necessary. This is different from the specialization of development engineers, so having engineers in-house perform annotation work alongside their primary duties does not efficiently utilize the engineers' expertise and leads to a decrease in the productivity of their original engineering tasks.
Annotation itself does not require much expertise, but to perform annotation, expertise in managing personnel and tasks is necessary. Since it differs from the expertise of development engineers, having engineers in-house perform annotation tasks alongside their primary duties means that the engineers' expertise cannot be utilized efficiently, leading to a decrease in the productivity of their original engineering work.
Annotation itself does not require much expertise, but to perform annotation, expertise in managing personnel and tasks is necessary. Since it differs from the expertise of development engineers, having engineers in-house perform annotation tasks alongside their primary duties means that the engineers' expertise cannot be utilized efficiently, leading to a decrease in the productivity of their original engineering work.
Therefore, outsourcing annotation tasks allows engineers to focus their expertise on their primary duties, which can ultimately lead to cost reduction and increased productivity.
No labor costs or management expenses
Even if you gather annotation personnel in-house, managing a large volume of annotations requires a significant number of staff, leading to high costs and effort in management areas such as quality and progress control. Additionally, unless annotation tasks are consistently generated, these resources can become surplus. Furthermore, since annotation requirements vary by project, there is a need for personnel training, which incurs additional educational costs. By outsourcing, you can expect to reduce these labor and management costs. Let's take a detailed look at the benefits of such outsourcing.
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2-1. Cost Reduction and Time Savings
In addition to what has been mentioned so far, the labor cost per unit for development engineers and annotators differs significantly. Therefore, even when including management fees and profits from outsourcing vendors, it is often possible to reduce costs compared to performing annotation in-house, and the cost-saving effect becomes more pronounced as the volume of work requested increases. Additionally, many annotation vendors have specialized experience in annotation tasks, so they possess know-how to improve productivity per person without solely relying on increasing personnel. Thus, by outsourcing, it is possible to not only secure time within the company but also expect a reduction in the overall delivery time of annotation tasks while keeping costs down.
2-2. Reducing Management Burden
Handling large-scale data annotation tasks typically involves a significant number of personnel. To ensure quality while adhering to the schedule, various management tasks are required, such as preparing work manuals and progress management, regardless of the type of annotation. Especially in the early stages of the work, there are many questions and answers with the annotators. This management not only takes more time than expected but also requires experience and expertise to proceed efficiently. If you can entrust this to an experienced external vendor, the management burden can be alleviated.
2-3. Improvement and Stabilization of Quality
Although automation is progressing, annotation is still often done manually, and the experience and skills of annotators, as well as their understanding of specifications, along with variations among annotators, greatly affect the overall quality of the training data. If you have experienced annotators and outsourcing vendors that can manage personnel appropriately, you can achieve high-quality annotations based on specifications and work instructions. By outsourcing to such vendors with experienced annotators, you can expect stable, high-quality training data.
3. 7 Tips for Choosing an Annotation Outsourcing Partner
When outsourcing annotation, choose a company that aligns with your AI development goals, as well as requirements for quality and security. Here, we will explain seven points to consider when selecting an outsourcing partner.
3-1. Is it suitable for our company's requirements?
The types of annotations, data formats, tools you want to use, and the scale of the project can vary greatly. Because this is somewhat obvious, it is easy to overlook confirming these details, so it is important to remember to check whether the outsourcing partner meets your company's requirements.
3-2. Expertise and Experience
In the case of specialized annotations, such as medical image annotations, text, and language annotations, expertise is often required. Let's check whether there is a track record of annotations that align with your company's objectives. If you work with a company that has high expertise and experience in specialized annotations, you can receive annotations tailored to your needs, or even appropriate advice if your company lacks such know-how or experience.
3-3. Quality Control
It is important to ensure that quality control is conducted properly. This includes not only the project management system, checking methods, and checking structure, but also the thorough management of change information related to specification changes. When feedback such as corrections or changes is provided from our company, it is essential to confirm how that information is communicated to the annotators, how it is reflected, and how it is verified. It is also important to check the processes and methods of information management.
info! The quality of teacher data is important as it depends on the original data being collected
One important point to note here is the quality and quantity of the data provided to the vendor. Let's gather as much data as possible. The amount of data required varies depending on the objectives you want to achieve with AI, so there is no fixed number of items; however, for images, thousands to tens of thousands of images may be necessary. Additionally, regarding the quality of the data, it is important to prepare a variety of types and patterns of data without bias.
For example, let's say you request car annotations. In such cases, instead of preparing data solely from images in the city, by providing images from various situations such as on highways or on rainy days, the AI's learning deepens, and the recognition accuracy improves for images in various situations. For more details, please refer to the related blog.
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3-4. Security
It is important to ensure that appropriate security measures are in place. Some companies may only accommodate remote work by cloud workers. It is possible that sufficient security measures or compliance with the company's security requirements cannot be ensured through remote annotation. In such cases, check whether they also support on-site work, such as security rooms or client site assignments. Additionally, it is essential to choose a company that implements multifaceted security measures, including not only hardware but also security training for workers and the establishment of information security management systems.
3-5. Scalability
While it is often not given much importance at the PoC stage, let's consider the possibility of expanding the scale of annotation as we move to the next phase, and check the number of personnel that can be secured by the outsourcing partner and the delivery time for the expected scale of annotation. It would also be ideal to confirm whether they can respond in urgent situations.
3-6. Communication
In annotation, even after defining requirements and creating specifications, various exceptions and edge cases may arise as work progresses. Frequent Q&A and feedback with outsourcing partners often occur, making it important not only to ensure smooth communication but also to manage, update, and communicate information efficiently. It is also essential to verify whether information can be shared appropriately and efficiently through communication methods tailored to requests, such as the use of chat tools and centralized information management.
3-7. Price and Pricing Conditions
Depending on the outsourcing partner, not only the fees for annotation services but also the pricing methods for file unit costs, annotation unit costs, hourly rates, and other pricing units may vary. When selecting an outsourcing partner, it is recommended to gather multiple quotes from different companies while aligning the conditions for data quantity, delivery time, checking methods, and the number of objects per file, as well as the conditions for the estimate request. Additionally, since fees often vary based on the working conditions such as remote work or security rooms, it is important to compare prices accordingly.
4. Achievements in Human Science
Here, we would like to introduce an example of our past achievements. We hope this serves as a reference when considering outsourcing.
4-1. Project Overview
Conversation Text Classification Annotation
4-2. Issues and Proposals
Issue
During our consultation, the following issues were raised by our clients.
・This is the first time outsourcing annotation, and there are concerns about pricing, quality control, the review system, and whether our feedback and requests will be properly reflected.
・Annotation is highly ambiguous and does not have an absolute correct answer. Therefore, there is expected to be a lot of variability among individuals, and we are struggling with how to annotate to obtain accurate training data and how to check it.
Proposal Details
Based on the challenges faced by our customers, we have made the following proposals.
・Proposal for quality control, reflection of feedback, and management system. Presentation of a clear estimate with conditions and unit prices, along with flexible changes to the estimate content according to customer requests.
・Due to the high ambiguity and the lack of absolute correctness in annotations, we propose a triple pass + check where three annotators (experienced in similar tasks) perform the same annotation instead of the usual correctness check.
・Creation of annotation specifications by our company and alignment with the customer.
・Establishment of a rapid feedback system through the opening of a chat with the customer.
4-3. Effects After Implementation / Customer Feedback
"I am very satisfied with the quality of the annotations and your company's response during the project. It was very helpful that you responded promptly to our requests and corrections during the work. From the moment I first approached you, I felt that Human Science was a team of experienced professionals, so I thought it would be safe to rely on you. I truly feel that it was the right decision to trust Human Science."
5. Human Science Annotation, LLM RAG Data Structuring Agency Service
A rich track record of creating 48 million pieces of training data
At Human Science, we are involved in AI model development projects across various industries, starting with natural language processing, including medical support, automotive, IT, manufacturing, and construction. Through direct transactions with many companies, including GAFAM, we have provided over 48 million high-quality training data. We accommodate various types of annotation, data labeling, and data structuring, from small-scale projects to long-term large projects with a team of 150 annotators, regardless of the industry.
Resource management without using crowdsourcing
At Human Science, we do not use crowdsourcing; instead, we advance projects with personnel directly contracted by our company. We form teams that can deliver maximum performance based on a solid understanding of each member's practical experience and their evaluations from previous projects.
Supports not only annotation but also the creation and structuring of generative AI LLM datasets
In addition to labeling and annotation for identification systems for data organization, we also support the structuring of document data for the construction of generative AI and LLM RAG. Since our founding, we have been engaged in manual production as a primary business and service, leveraging our unique know-how gained from a deep understanding of various document structures to provide optimal solutions.
Equipped with a security room in-house
At Human Science, we have a security room that meets ISMS standards within our Shinjuku office. Therefore, we can ensure security even for projects that handle highly confidential data. We consider the protection of confidentiality to be extremely important for all projects. Even for remote projects, our information security management system has received high praise from our clients, as we not only implement hardware measures but also continuously provide security training to our personnel.
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