What is training data? How is it different from learning data? How much do you need? We explain how to create it in-house or outsource it, and what to be careful of when collecting it!

Supervised learning, which can be called the standard for AI model creation, requires training data, which is a set of "example problems" and "correct answers." Training data has a significant impact on the accuracy of the completed AI model.

Therefore, high-quality training data is indispensable for developing high-precision AI models. However, obtaining training data that matches a company's specific needs is surprisingly difficult. Furthermore, even when told to prepare "large amounts" of "high-quality" data, many people worry that there are too few clues on where to even begin their considerations.

In this article, we explain everything from the overview of training data to how to create it and points to note during creation.

1. What Is Training Data?

Training data is a training dataset used for machine learning of AI models. Training data consists of a set of "conditions (explanatory variables)" and "correct answers, or labels (objective variables)," and is used for "supervised learning."

Supervised learning is machine learning where an AI model is trained to associate "conditions" with "correct answers." The performance of the model varies greatly depending on the quality and quantity of the training data, as well as the selected algorithm.

For example, the methods used to train ChatGPT, which has been a social phenomenon since 2022, also include supervised learning. Training data for ChatGPT consists of sets of large amounts of text data and the appropriate response or following text for that data.

The AI model learns the relationship between "conditions" and "correct answers" from the data and performs predictions or classifications on unknown data. The AI model learns what kind of patterns become correct answers.
Difference between with and without training data
Supervised learning is a method of training an AI model using data with correct labels to give the model the ability to achieve specific tasks. On the other hand, unsupervised learning is a method of training a model using data without correct labels to automatically identify patterns or structures within the data.

Supervised learning is suitable for training models to solve clear tasks, while unsupervised learning is useful for exploratory analysis of data and discovering new insights.

For example, in the development of a spam filter using supervised learning, a large number of emails labeled as spam or not are supplied to the AI model, and over time, the model improves its ability to identify spam emails more accurately.

On the other hand, unsupervised learning is mainly used for clustering, dimensionality reduction, association analysis, and more. An example is a model that determines latent patterns from customer purchase history data and automatically identifies customer segments. Supervised and unsupervised learning are used differently depending on the business scenario, the purpose of the AI model, and the quantity and quality of available data.

Difference Between Training Data and Learning Data

Learning data is the overall dataset used for machine learning of an AI. On the other hand, training data refers to the data used specifically for "supervised learning."

Learning data covers a wide range of types, such as customer behavior history and sensor data, and includes items that have not been assigned labels. In contrast, training data is data that has been labeled for a specific task, such as an image and its classification label.

In other words, training data can be said to be a part of learning data. Learning data is broader and is used not only for supervised learning but also for unsupervised learning, reinforcement learning, and more.

2. How Many Training Data Are Needed?

The required number of training data points varies depending on the purpose of the AI system you want to create and the required accuracy.

For basic identification tasks or cases where accuracy is not highly required, several hundred pieces of training data may be sufficient. However, in tasks where high precision is required, such as advanced image recognition or natural language processing, it is not uncommon to need thousands to tens of thousands of data points for a single object.

Furthermore, not only the number of training data pieces but also the quality and balance affect the required quantity. For example, in anomaly detection, the data balance between abnormal cases and normal cases is important, and if this is imbalanced, it becomes difficult to train a high-precision model.

While the required number also changes depending on the balance and quality of the training data, if high precision is required, a large amount of data is necessary.

Open data is basically available for anyone to use. However, please check the terms of each dataset regarding whether commercial use is permitted.

By utilizing open data, data collection can be completed without any effort or cost. However, because appropriate open data for all industries and fields does not necessarily exist, there are many cases where open data cannot be utilized.

Additionally, it would be difficult to build a model optimized for your company using only open data. Therefore, to construct a high-precision model, ingenuity is required, such as utilizing it in combination with data collected independently.

3. How to Obtain Training Data

There are broadly three ways to obtain training data.

① Use in-house data

If you have in-house data, actively utilize it. Since you can use data that is best suited for your company, you can improve the precision of your AI model efficiently.

However, to utilize it as training data, annotation processes such as assigned "correct answers" and data formatting must be performed to make it ready for the AI model to learn. Caution is required as it can rarely be used as-is.

② Outsource to a specialized company

Training data can also be collected and created by outsourcing to a specialized company. The performance of training data changes significantly depending on the overall balance and the quality of the "correct answers."

In the following cases where in-house production is difficult, consider outsourcing.

・There is no data person or analyst in the company
・Large amounts of datasets are required
・There is little data available for use within the company

③ Purchase datasets

If unique in-house data is not necessary, you may purchase pre-created datasets. Since you can obtain data with "correct answers" already assigned, you can have the AI model learn from it as-is.

Also, you might be able to use open data that is available for free.
However, you must correctly determine whether it is the data necessary to construct the AI model your company is seeking. Additionally, check for requirements regarding credit notation and the necessity of commercial licenses. If it can actually be utilized, the effort for data collection is eliminated, thus significantly reducing labor.

4. 3 Steps to Create Training Data

We will explain the steps to create training data in order.

① Clarification of purpose

First, clarify the purpose of developing the AI model. Once the purpose is clear, it becomes apparent what kind of AI model should be made, thus determining the types and quantity of data to be collected and the learning method.

Entering data collection while the purpose is vague and having to make corrections halfway through can result in the collected data going to waste. Data collection and the subsequent annotation involve extremely large amounts of labor. To avoid wasting these, clarify the purpose at the beginning.

② Data collection

Once the purpose is clear, collect data accordingly. The number of data pieces to collect varies depending on the required accuracy and number of elements. Decide on the number to collect while listening to the in-house data person or experts at an outsourcing partner.

Furthermore, even if the number of data points is large, accuracy will not be high if the quality or balance of the data is poor. Try to collect data while paying attention to quality and balance, without being preoccupied only with the quantity of data.

③ Labeling (Annotation)

Labeling is the work to create "correct answers." Also known as annotation, it is the most important stage in the training data creation steps. It is necessary when performing supervised learning where correct answers are required.

For example, if you want to create an AI model that recognizes cars from images, you must teach it the shape of a car. Therefore, you teach the AI model the "correct answer" of a car by labeling.

There are various labeling methods depending on the type of data, as follows:

As with data collection, the quality of labeling significantly impacts the performance of the AI model. In recent years, specialized tools have been developed and are attracting attention for allowing labeling with less labor. However, tools are merely developed to allow work to be done efficiently.

Since accuracy depends on the skill of the worker, try to tackle it after conducting thorough education or leaving it to a specialized company.

5. Points to Note When Creating Training Data

When creating training data, it is smooth to pay attention to the following points:

① Ensure data quality and quantity

The quality and quantity of training data significantly impact the performance of an AI model. First, if the quantity is insufficient, it becomes difficult to grasp the features of elements, resulting in low prediction accuracy. Next, if the quality is low, each piece of data cannot be fully utilized, and performance becomes low relative to the quantity.

Data quality refers to elements such as the following:

If data balance is poor, accuracy can sometimes worsen as you increase data. Furthermore, if annotation accuracy is low, recognition accuracy becomes low.

To maximize the performance of your AI model, try to collect data after making a plan with experts to ensure data quantity and quality.

② Do not infringe on copyrights or privacy

There is a possibility of infringing on copyrights and privacy during training data creation.

Large amounts of learning data are necessary for AI development. According to the view of the Agency for Cultural Affairs, it is possible in principle to have an AI learn without the permission of the copyright holder if "it is not for the purpose of enjoying the thoughts or emotions expressed in the work."

* Agency for Cultural Affairs "AI and Copyright"
https://www.bunka.go.jp/seisaku/chosakuken/pdf/93903601_01.pdf

However, if you intentionally try to infringe on copyright, such as by making large amounts of images mimicking a work with an AI and developing an AI using those as learning data, it can result in a violation of the law. Since discussions on AI and copyright are still shallow, there are many parts that are not clear. Therefore, check the data usage permission and obtain permission from the copyright holder when necessary.

Furthermore, there is a risk of infringing on privacy during training data creation. Pay particular attention during annotation work.

Data to be annotated may include personal information. If this leaks, the individual whose personal information is listed may suffer disadvantages.

To avoid such situations, handle data with sufficient care. Pay special attention to data related to personal information and set clear guidelines for relevant parties.

③ Take security measures

Some training data may include personal information or confidential information. If these leak, companies or individuals may suffer disadvantages, so take sufficient security measures to prevent information from leaking.

Even with a specialized annotation company, there is a risk of information leakage if the security structure is not established.

Some specialized companies request annotation work from crowdsourced workers. In the case of data including sensitive information, it might be better to request a company that performs annotation in-house to reduce the risk of information leakage.

④ Unification of annotation work rules

When rules are unified, quality is kept consistent. This contributes to improving the precision of the AI model.

If you perform annotation in-house, it is a good idea to unify work rules by creating manuals or similar materials. If everyone tackles annotation work with different procedures, it becomes difficult to grasp progress. With unified rules, it becomes clear what workers should do, and the speed of work improves.

Furthermore, if work rules are unified, employees can share parts they do not understand, preventing situations where too much burden is placed on experts or knowledgeable persons. To proceed with work smoothly, it is better to have annotation work rules unified.

⑤ Construction of an annotation work management system

At the stage of performing annotation, instead of just labeling, someone to manage progress and quality is necessary. Neglecting this leads to projects not proceeding on schedule or result in low-quality AI models.

Annotation work takes an enormous amount of time. To avoid wasting that time, it is important to tackle annotation work after firmly constructing a management system.

6. Summary

Training data is indispensable for performing supervised learning. Although creating training data requires significant effort, if you can make the most of the completed AI model, you will gain even greater benefits.

However, if the quality of training data is low, the quality of the AI model will also be low. Even if you spend enormous effort, if you cannot reach the target precision, it can result in an AI that is completely meaningless.

If there is no data expert in the company, outsourcing to a specialized company allows for the development of higher-quality AI systems and is lower cost in the long term.

Author

 

Toshiyuki Kita
Nextremer VP of Engineering

After graduating from the Graduate School of Science at Tohoku University in 2013, he joined Mitsui Knowledge Industry Co., Ltd. As an engineer in the SI and R&D departments, he was involved in time series forecasting, data analysis, and machine learning. Since 2017, he has been involved in system development for a wide range of industries and scales as a machine learning engineer at a group company of a major manufacturer. Since 2019, he has been in his current position as manager of the R&D department, responsible for the development of machine learning systems such as image recognition and dialogue systems.

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