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Datasets are an essential part of building AI. Datasets are used to train AI to make decisions and to evaluate its accuracy after training, which requires a huge amount of data. Open data is available for free, but given the time and effort required to collect it, it is more efficient and accurate to build AI by purchasing datasets. This article explains how to obtain datasets for use in AI, including self-created datasets, as well as the benefits and considerations of purchasing them.
1. What are datasets in building AI?
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There are three main types of datasets used when building AI:
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Training set
A training set is the data used to train an AI. Since the quality of the training set directly affects the accuracy of the AI, the dataset needs to be as high quality as possible. In order to build a high quality dataset, you need not only accurate but also correct labels, while also ensuring that the data is rich in variation. Building such a dataset can be very time consuming, which is why many people consider buying one.
Validation set
When training AI, there are some settings, called parameters, that need to be adjusted manually to help the AI learn better. The validation set is a part of the data that checks that these adjustments are working. It helps you see how well the AI is learning before it's fully trained. Sometimes part of the training data is saved and used as a validation set to test the AI during the training process.
Test set
The test set is the dataset used to evaluate the AI after it has been trained on the training set. It is important to check whether the AI can work well in its intended environment and goals, as it needs to simulate real-world conditions.
2. How to obtain dataset for AI training
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There are two ways to get datasets: "creating your own" or "purchasing"
Creating your own dataset
To create your own dataset, follow these steps:
①Search for open data. If no suitable data exists for your purpose, collect your own data
②Choose or create an annotation tool
③Perform the annotation
Open data refers to <strong>data that can be used for free, even for commercial purposes.</strong> If open data is available that matches the purpose of the AI you are developing, you can move directly into the development phase.
Many open datasets can be found on the following websites.
DATA GO JP
https://www.data.go.jp/
Statistics Bureau of Japan
https://www.stat.go.jp/
If open data cannot be found, you will need to collect the data yourself. In addition, if you find open data that does not have the required labels, you will need to perform a process called ‘annotation’. Annotation is a manual task of applying correct labels to data. To perform annotation, you will need a tool. You can either use open source platforms such as GitHub or create your own in-house.
Purchasing a dataset
When purchasing a dataset, the first step is to choose the company you want to buy it from and decide what you want it to do. For example, you may need them to collect the data or to handle annotation. Many companies offer consultancy from the initial stage of defining requirements, so you can create a high quality dataset that suits your needs, even if you don’t have extensive AI expertise.
3. Benefits of purchasing AI dataset
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The benefits of purchasing a dataset is that you can obtain high-quality, fit-for-purpose data without having to go through the time consuming process of collecting and annotating the data yourself.
No need to collect data
When collecting data, you start by looking for open source data, but much of it is not available for commercial use. If you can’t find what you’re looking for, you’ll have to collect the data yourself, but this can easily lead to bias in the dataset and result in a dataset with little variation. When you purchase a dataset, you can request that the data be collected for you, so you can create a dataset with a wide range of variation.
No need for annotation
Once you have collected a dataset, <strong>you also need to decide on the criteria for applying the correct labels</strong> when annotating. For example, if you want to train an AI to detect the position of a person, you need to decide, based on the AI's function, whether the correct answer is a rectangle around the whole body from head to toe, or just the upper body. You also need to consider standards for patterns that are difficult to annotate, such as when people overlap, and this requires specialist knowledge. When you purchase a dataset, you can consult with experts about the annotation process, enabling you to create a dataset without deep technical knowledge.
High quality data to suit your needs
Data collection and annotation require you to work with huge amounts of data, so if you are not used to doing this kind of work, it is easy to make mistakes with the correct labels. If you purchase the data, you can leave the work to specialists, so you get high quality data with less bias and no errors in getting the labels right. You can also discuss data delivery, so you can get data that suits your internal systems and objectives.
4. Things to consider when purchasing a data set
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When you purchase a dataset, you will be charged a fee that corresponds to the content of your request. Before purchasing, it's important to consider the following things carefully
| ①Search for open data ②Check that the data you are purchasing is suitable for your purpose |
①Search for open data
If there is suitable open data available, there’s no need to purchase a dataset, so it’s crucial to conduct a thorough search. Check not only Japanese websites, but also those of overseas companies and academic papers. Academic papers often list the datasets used, which can be a useful reference. However, be sure to check for commercial use, as many of the datasets in academic papers are not for commercial use. It is also a good idea to contact the data provider by email or phone to enquire about the availability of the dataset.
②Check that the data you are purchasing is suitable for your purpose
Not all companies that sell datasets can handle all types of data collection and annotation. Depending on the company, the service may be limited to only audio or only images of individuals of a particular group. Check that the service from the company you purchase from is suitable for your purpose, for example, whether it is appropriate to use data with individuals of a particular nationality when training the AI to recognise people. Also, when setting the annotation criteria, make sure they are aligned with the goals of your AI to avoid inconsistencies. If it is necessary to go back and redo the annotation work, you may be charged an additional fee. If you plan to use the service for a long period of time, make sure you have an environment where you can regularly review the deliverables.
5. Conclusion
We have looked at how to obtain datasets for use in AI, including those you create yourself, and the benefits and considerations you should be aware of when purchasing them.
When creating your own datasets, you should follow these steps:
①Search for open data. If none are available, collect the data yourself.
②Select or create an annotation tool
③Perform annotation
Each step requires specialist knowledge and personnel.
The main advantage of buying a dataset is that you can outsource all the steps involved in creating the data, ensuring high quality data suitable for your purpose. However, as companies offer different types of datasets, you should check in advance that they can provide data that suits your specific needs.
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