機械学習(AI)エンジニアの将来性は?需要が高まる産業や必要なスキルも解説What is the future of machine learning (AI) engineers? Explaining the industries where demand is growing and the skills required.

Machine learning (AI) engineers are among the engineers involved in AI development, and are mainly involved in the implementation and development of machine learning. Although it is a position that is attracting a lot of attention as the demand for data analysis and DX expands, some may wonder if there is really a future for them if they change their jobs now.

In this article, we will discuss the future of machine learning (AI) engineers. We will also explain the industries where demand is expected to increase in the future, the skills required, and the characteristics of those who are suited for the job.

Of the many different types of jobs available, machine learning (AI) engineers are said to have a promising future. Why are machine learning (AI) engineers expected to have a promising future? We will explain the reasons, taking into account the current situation.

The AI industry's market size is expanding

The market size of the AI industry is expanding on a global scale. According to the "2023 White Paper on Information and Communications" published by the Ministry of Internal Affairs and Communications, the global AI market (sales) is expected to grow 78.4% year-on-year to approximately 18.7 trillion yen in 2022, with accelerated growth expected until 2030.

In Japan, the domestic market for AI systems was approximately 390 billion yen in 2022 (up 35.5% from the previous year), and is expected to expand by more than 1 trillion yen by 2027. As the market grows, demand for machine learning (AI) engineers will naturally increase.

In addition, although affected by the spread of the new coronavirus infection after 2020, as of 2024, many companies are increasingly willing to invest in DX promotion. Services utilizing generative AI are also expanding, and machine learning (AI) engineers can be found in a variety of industries.

Supply is inadequate for the demand for machine learning (AI) engineers.

While an increasing number of companies are working to train and secure IT personnel, there is currently a shortage of AI personnel. AI personnel, on the other hand, are experts in designing and developing AI systems such as machine learning, deep learning, and natural language processing, etc. Expertise in AI technology is necessary, but the training of AI personnel has not kept pace with the current situation.

As explained above, the AI market continues to expand year by year, and companies are looking for AI professionals to help them develop AI-enabled systems. Despite the high demand, the supply of AI personnel is not keeping pace with the demand, resulting in a shortage of AI personnel.

The Ministry of Economy, Trade and Industry is focusing on AI human resources development.

While demand for AI personnel is increasing in a wide range of industries, a shortage of human resources is becoming an issue. To solve this problem, the Ministry of Economy, Trade and Industry (METI) and others have begun to focus on AI human resource development.

For example, "Manavi DX," a portal site that provides learning content to acquire knowledge about AI and digital technology, allows people who have never had the opportunity to learn digital skills to do so. In addition, in education and training courses offered by private businesses for working people, mainly in the IT and data fields, it has been recognized as a Fourth Industrial Revolution Skill Acquisition Account (reskill course), and efforts to support career development are also being implemented.

In the future, it can be expected that there will be a stronger movement to not only train AI personnel but also to treat them well so that they will have more opportunities to play an active role as AI personnel. For these reasons, the future of machine learning (AI) engineers is also promising.

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While the future potential of machine learning (AI) engineers is promising, it is not entirely free of concerns. As mentioned earlier, the Ministry of Economy, Trade and Industry (METI) is currently focusing on training AI personnel to solve the problem of a shortage of AI personnel, However, as the number of machine learning (AI) engineers increases in the future, competition may intensify.

There are also those who say that the development of AI may render machine learning (AI) engineers themselves unnecessary. For example, if AI can develop new AI, engineers may become unnecessary, since programming is already automated at this point.

However, program development by AI is not perfect; even though AI can do the programming, human hands will ultimately be needed.

In particular, machine learning (AI) engineers are primarily responsible for implementing and operating machine learning algorithms on AI, rather than simply programming with AI. They will also be responsible for developing algorithms, collecting data, and building the environment necessary to run the machine learning models. There is no concern that these jobs will be replaced by AI.

While demand for machine learning (AI) engineers is growing in all industries, the following industries are expected to be in greater demand in the future

Medical and Nursing Care

The medical and long-term care industries are already making use of AI technology. For example, AI has been developed to predict whether a patient with mild cognitive impairment will subsequently progress to Alzheimer's disease. It predicts the progression of the disease by analyzing brain images, and its accuracy is as high as 88%.

Another service has also been developed in which an AI watches over people in a facility. This service accumulates and analyzes data on emergency detection patterns and activates sensors when an abnormality is detected. This service, which utilizes multiple sensors, can detect abnormalities while protecting privacy in facilities where the use of cameras is difficult from the standpoint of protecting personal information.

Although it is humans who directly diagnose, treat, and care for patients, there are high expectations for the use of AI to support diagnosis and treatment. Especially in Japan, where the population is aging, AI technology is expected to become more widespread in order to reduce the burden on medical and nursing care workers.

Agriculture and Fishing

The agriculture and fishery industries are currently facing a labor shortage. AI technology is beginning to be introduced to reduce the workload and improve efficiency.

For example, in agriculture, AI technology is being used to predict the timing of harvests and to develop automatic harvesting equipment, etc. If technology can be visualized through AI, it will be easier to pass on skills.

In the fishery industry, AI technology is being used to develop smart feeders that automatically feed fish in the aquaculture industry, to predict landings data, and to narrow down fishing areas. Machine learning (AI) engineers will also be able to play an active role as various AI devices and systems are being developed to solve the problem of labor shortages.

Manufacturing Industry

AI technology is also being used in the manufacturing industry to address labor shortage issues and an aging workforce.

For example, AI can be used to automate "robot teaching" by introducing AI to industrial robots. Robot teaching is the process of teaching a robot to perform a task, which was previously performed manually by humans.

The self-learning function of AI can automatically learn product patterns and other information, enabling highly accurate handling and inspection of products. In addition, by utilizing a system in which AI automatically detects robot abnormalities, it is possible to predict robot failures in advance, enabling maintenance to be performed at the appropriate time.

Logistics and Infrastructure

AI-based logistics systems are being introduced to solve various problems faced by the logistics industry. By incorporating AI technology into logistics systems, it is possible to reduce warehouse management costs and optimize delivery routes.

Some major companies have already introduced automated robots that carry goods in warehouses, or systems that predict the optimal pickup and delivery sequence to improve driver work efficiency. Demand for AI technology in the logistics and infrastructure industries is expected to continue to grow, and machine learning (AI) engineers will also be in high demand.

Financial Industry

As AI technology is being adopted in a wide range of industries, the financial industry is also expected to adopt AI. In the case of the financial industry, AI is needed not simply to improve operational efficiency and reduce costs, but also to hold and update accurate data on changing customer needs.

For example, there is an investment service called "robo-advisor," which provides market forecasts and portfolio recommendations based on the vast amount of data and analysis collected by AI. There is also a growing trend to prevent crime by introducing AI-based fraud prevention systems to detect unauthorized withdrawals and unauthorized accounts.

In order to be a successful machine learning (AI) engineer, there is certain knowledge that you should acquire. This section explains what specific knowledge you should acquire.

Knowledge about machine learning libraries

A machine learning library is a collection of programs for building machine learning models. While it is time-consuming to create a machine learning program many times in order to build a machine learning model, having a library makes it possible to use the program and develop it efficiently, so it is important to acquire knowledge about machine learning libraries.

There are various types of machine learning libraries, each with different characteristics.
 

• cikit learn: A library that can be used for algorithms for machine learning in general
• TensorFlow: Library that also supports deep learning (deep learning)
• Numpy: Numerical calculation library for Python
• pandas: Library for data processing and analysis that runs on Python
• matplotlib: Data visualization library for Python

Knowledge of Databases

Databases are data that are collected, organized by management programs, and made available for retrieval whenever needed. Moreover, to achieve the desired accuracy, a huge amount of data must be handled. Therefore, it is a good idea to acquire knowledge about databases.

Database management programs are called "DBMS" and require a database language to handle them. Currently, SQL is the most widely used database language. MySQL is one of the most commonly used database languages for machine learning because it can be used regardless of the operating system, is highly scalable, and allows for speedy searches.

Knowledge of the Cloud

Machine learning (AI) engineers deal with large amounts of data, and often use cloud systems to analyze this data efficiently. This is because the amount of data that can be handled is limited only by the capacity of the computer at hand. The use of cloud systems is indispensable for handling huge amounts of data.

Although cloud engineers do not need as much specialized knowledge as cloud engineers, if you can afford it, you should also acquire knowledge of cloud services such as AWS and Google Cloud.

Knowledge of Data Models

A data model is like a blueprint for storing real-world data in a database. The data model defines the format and structure of the data.

Because machine learning (AI) engineers deal with large amounts of data, it is also important to define the format and structure of the data, as well as the relationships among the data. Designing databases and data structures for data analysis is also the job of machine learning (AI) engineers.

Knowledge of statistical analysis

Knowledge of statistical analysis is also necessary when developing machine learning algorithms. In particular, knowledge of statistics and data evaluation methods makes it easier to understand technical terms.

However, even if you do not have knowledge of statistical analysis, the development itself is possible. Even so, there are many terms that are common to statistics, such as median and standard deviation, so there are situations where what you have learned in statistical analysis can be used in machine learning.

If you want to become a machine learning (AI) engineer, you should also know what kind of person is suited for this position. Here are three characteristics of a suitable candidate.

People who have no resistance to AI

Machine learning (AI) engineers are constantly confronted with AI. Therefore, people who have no resistance to AI are suited for the position of machine learning (AI) engineer. This position is suitable for those who are interested in AI and want to contribute to society by making use of AI technology.

People who are good at mathematics

The foundation of AI technology is based on mathematics and logical thinking. If you are good at mathematics, which can be called the foundation of your work, and have logical thinking skills as well, you are suited to be a machine learning (AI) engineer.

In reality, statistical analysis can be done within libraries, but a mathematical background is essential for understanding algorithms and analyzing data. In particular, linear algebra, probability, and statistics are essential for understanding and implementing AI models. For this reason, people who are good at mathematics are suited to be machine learning (AI) engineers.

People who are good at keeping up with new information

AI technology is developing day by day, and many new methods and tools are emerging. It is important to actively catch up with and learn new information in order to stay on the cutting edge of technology. Therefore, a good fit for a machine learning (AI) engineer will be someone who is good at keeping up with the latest information and someone who is always looking to acquire new knowledge.

For example, it is important to keep up with the latest trends and research through reading and participating in webinars and forums.

In this issue, we have introduced the future of machine learning (AI) engineers. With the increasing use of AI in a wide range of industries, the future of machine learning (AI) engineers is promising. If you want to become a machine learning (AI) engineer from another engineering profession, it is important to acquire knowledge about machine learning libraries and databases, and to keep up with the latest technologies.

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