■ A problem that hinders the promotion of DX/AI: “Shortage of human resources”

As each company advances its digital transformation efforts, an increasing number of companies are promoting initiatives using AI to utilize data. On the other hand, there is a shortage of data scientists who handle AI throughout Japan. The number of cases where the project stops because it doesn't run is increasing.

When incorporating AI models into your business, you need to understand how they differ from traditional IT systems.

・Data changes as the business environment changes. Therefore, AI models require continuous maintenance in line with changes in data.

・The AI model that brings about business impact differs according to the difference in the business environment. Therefore, there is no AI model that achieves the same high accuracy for any data, and it is necessary to develop and operate an AI model that is highly accurate for specific task data.

AI models need to be developed individually according to changes in the business environment (changes in data). Also, if maintenance is not performed continuously, the accuracy will decrease. Therefore, AI models should not be “made once and finished”, but should be developed with consideration for the uniqueness of the data, and should be considered on the premise of continuous maintenance thereafter. In addition, in order to perform maintenance, a system and human resources are required for data scientists to periodically perform accuracy checks and model tuning.

On the other hand, there are few companies in Japan that say they have an abundance of data scientist human resources, and many companies are short of human resources. One way to solve these issues is to utilize the data scientist community.

■What is the data scientist community?

The data scientist community refers to the community of people involved in machine learning and data science around the world, such as Kaggle (Google).

Discussions such as what kind of approach can be taken to develop the optimal AI model for the problem are being held, and information such as new algorithms and feature value design methods are being exchanged on a daily basis.

In addition, companies can hold competitions by providing challenges and data sets, and members of the community can compete for scores and carry out model development.

Various data scientists can simultaneously develop AI with different approaches to one problem.

AI model development using this data scientist community is considered one of the best practices. There are three main reasons.

1. 多様な専門性を持つ人材プールから、解決したい個別の課題に合わせて最適な人材をクラウドソーシングできる
2. Since it is possible to develop models while exchanging various opinions, the possibility of carrying out innovative development through open innovation increases.
3. While avoiding the bias* of data scientists, you can compare multiple different approaches in parallel in a short period of time and select the best approach.

*Bias: A bias in thinking that arises from past experiences. Example: For the 〇〇 data set, the??algorithm should definitely be suitable! believing that

Utilizing the data scientist community and effectively promoting the development and operation of AI is one of the keys to advancing digital transformation.

If there seems to be a problem due to human resources and skill sets in AI development and operation, why not consider using the data scientist community once?

■AI development using CrowdANALYTIX data scientist community supported by Macnica

Macnica uses Macnica affiliated companies to develop AI for customers.CrowdNALYTIXWe collaborate with the company's data scientist community to help customers solve their problems.

In cooperation with the data scientist community, which has more than 25,000 data scientists worldwide, we develop optimal AI models for the different challenges of each customer, and operate and maintain them while providing abundant human resources.

▼ Business problem-solving AI service that utilizes the data science resources of 25,000 people worldwide
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