Tag: Machine Learning

What is Natural Language Processing (NLP)? Explaining how it works, what it can do, and examples of its use
Do you know what Natural Language Processing (NLP) is? It is the technology that enables AI to analyze human language, understand conversations, and generate text, and it is being utilized in various business scenarios.
In this article, we will comprehensively explain the overview, mechanisms, capabilities, and application examples of NLP. If you are considering implementing AI in your company, please read through to the end.
What is Natural Language Processing (NLP)?
Natural Language Processing (NLP) is a technology that enables AI to understand, analyze, and effectively utilize human language. By treating the words we use daily (natural language) as data, it makes these things possible.
A major characteristic of NLP is its ability to understand not only the structure of words and grammar but also context and nuances. This allows NLP to be used in various scenarios, such as question-answering in search engines, multilingual translation in translation apps, and conversations with chatbots.
In this way, NLP can be considered a crucial technology for enabling smooth communication between AI and humans.

Types of Natural Language Processing (NLP) and Their Capabilities
Natural Language Processing (NLP) can be broadly divided into two categories: “Natural Language Understanding (NLU)” and “Natural Language Generation (NLG).” Let’s take a closer look at the differences between them.
Natural Language Understanding (NLU)
Natural Language Understanding (NLU) is the technology that allows AI to comprehend human language. Specifically, it analyzes the words, grammar, and context contained within sentences and conversations to interpret the underlying intent and meaning.
Through the application of NLU, AI can consider the ambiguity and polysemy of language and perform accurate interpretations. In other words, NLU plays the role of the “receiver” of language, handling tasks like text classification, sentiment analysis, and intent recognition.
Natural Language Generation (NLG)
Natural Language Generation (NLG) is the technology that enables AI to produce natural-sounding sentences and conversations, much like a human would. It is used in various scenarios, such as the automatic creation of news articles or chatbots that generate appropriate responses to users.
By using NLG, AI can construct grammatically correct and coherent natural sentences based on input data and information. Thus, NLG serves as the foundation for AI to act as a “sender” of language.

Application Examples of Natural Language Processing (NLP)
Recently, Natural Language Processing (NLP) has been utilized in a wide range of business scenarios. This chapter introduces five representative use cases of NLP.
Conversational AI
One of the most representative use cases of Natural Language Processing (NLP) is conversational AI. By applying NLP to AI chatbots or virtual assistants, they can respond to user questions and requests in natural language.
Specifically, the system works by using Natural Language Understanding (NLU) to analyze the intent of the question and Natural Language Generation (NLG) to generate an accurate response. NLP is used in a wide range of situations, from handling customer support inquiries to processing voice commands through smart speakers.
Machine Translation
Machine translation, which performs translation between different languages, is also a significant application field of Natural Language Processing (NLP). For example, Google Translate, a representative translation tool, uses NLP to analyze text and provide translation results that consider grammar and context.
Especially since the introduction of models based on the previously mentioned Transformer, it has become possible to perform natural and highly accurate translations. In this way, NLP forms the foundation supporting global communication across language barriers.
Text Mining
Text mining, which extracts valuable information from large volumes of document data, is widely used in various business and research fields. For example, it can analyze posts on social media to grasp consumer sentiments and trends, or extract essential information from legal documents or academic papers. This enables efficient acquisition of insights to support decision-making.
Related article: What is Text Mining? A Simple Explanation of the Mechanism and Typical Analysis Methods!
Text Summarization and Classification
Text summarization and classification, such as summarizing news articles or long texts, and categorizing emails or reviews, are applications of Natural Language Processing (NLP) that aid in information organization. Specifically, AI extracts the important parts of a text to create a concise summary, or assigns appropriate tags based on the content. In our age of information overload, NLP is a vital technology supporting efficient information gathering.
Text Generation
Natural Language Processing (NLP) enables the efficient generation of high-quality text. Examples include generating captions based on images or creating creative texts like novels and poems. Furthermore, advanced models like the GPT series can produce natural-sounding text that seems human-written, leading to applications in entertainment and marketing.

The Mechanism of Natural Language Processing (NLP)
So far, we have introduced the overview and application examples of Natural Language Processing (NLP), but how does NLP actually work? In this chapter, we explain the mechanism of NLP.
Machine-Readable Dictionary
The foundation of Natural Language Processing (NLP) is the machine-readable dictionary, a database of word meanings and relationships. Machine-readable dictionaries store definitions, parts of speech, synonyms, antonyms, and more, serving as a guide for AI to understand language. By utilizing this, AI can interpret words based on their basic meanings and context.
Corpus
A corpus is a large dataset of actual language data. Examples of corpora include newspaper articles, blogs, and social media posts. AI learns how words and phrases are used by analyzing corpora, making corpora a critical element that significantly impacts the performance of Natural Language Processing (NLP).
Morphological Analysis
Morphological analysis is the process of breaking down sentences into their smallest meaningful units, such as words or phrases, and identifying the part of speech and role of each. For example, the sentence “The cat walks” would be broken down into “The (article),” “cat (noun),” and “walks (verb).” By performing morphological analysis, Natural Language Processing (NLP) clarifies the basic structure of a sentence, thereby improving the accuracy of subsequent processing.
Parsing (Syntactic Analysis)
Parsing, or syntactic analysis, is a technique for analyzing the relationships between words in a sentence to clarify its grammatical structure. For example, in the sentence “The cat chases the mouse,” “cat” is identified as the subject, “chases” as the predicate, and “mouse” as the object. Performing syntactic analysis allows AI to understand the overall meaning of a sentence more precisely.
Context Analysis
Context analysis is a technique for analyzing not just words and phrases, but also their meaning within the overall sentence or conversation. For example, if the word “bank” appears in a sentence, the context determines whether it refers to a financial institution or the side of a river. By considering context to interpret meaning, AI can achieve more natural conversations and text generation.
Representative Models Used in Natural Language Processing (NLP)
Even when we simply say “Natural Language Processing (NLP),” the models used are diverse. In this chapter, we pick and introduce five representative models used in NLP.
word2vec
word2vec is a technique for representing words as vectors (sets of numerical values), allowing the numerical capture of word meanings, similarities, and relationships. For example, it enables calculations like “king – man + woman = queen.” A characteristic of word2vec is its lightweight and fast nature, leading to its use in a wide range of applications, including text classification, sentiment analysis, and search engine optimization.
doc2vec
doc2vec is an extension of word2vec, with the major characteristic being its ability to vectorize entire sentences or documents, not just words. By using doc2vec, it becomes possible to quantify the features of a text, measure similarity between documents, and perform document classification. For instance, it is used for categorizing news articles or reviews, serving purposes that require understanding the overall meaning of a text.
RNN
RNN (Recurrent Neural Network) is a type of neural network (AI technology designed by mimicking the neural circuits of the human brain) that processes input data while considering its sequential nature. It maintains past data in an internal state and reflects it in subsequent processing, thereby learning the flow of context. For example, RNNs are a valid option for analyzing short sentences or performing simple time-series prediction.
LSTM
LSTM (Long Short-Term Memory) is a type of RNN specifically designed to capture long-range dependencies in time-series data or extended contexts. While the vanishing gradient problem is a major challenge for standard RNNs, using LSTM enables Natural Language Processing that considers long-term dependencies. It is suitable for tasks where understanding the meaning of long texts or temporal flow is crucial, such as chatbots and machine translation.
Note: The vanishing gradient problem refers to the issue where, during the learning process of a neural network, the gradients (differential values) used to update weights by backpropagating errors become extremely small, causing learning to stall in deeper layers.
Transformer
The Transformer is a model that has become the mainstream in current Natural Language Processing (NLP). It efficiently understands context by utilizing a Self-Attention mechanism. The Self-Attention mechanism allows all words in an input sentence to evaluate the relationships and importance among each other.
Because Transformers achieve high accuracy in tasks like translation, summarization, and text generation, they serve as the foundation for advanced models such as GPT and BERT. Unlike traditional RNNs and LSTMs, Transformers allow for parallel processing and offer high computational efficiency, demonstrating overwhelming performance in tasks involving large-scale data.

Points to Note When Using Natural Language Processing (NLP)
While Natural Language Processing (NLP) is a very convenient technology, there are several points to keep in mind when actually using it. This chapter introduces three precautions when utilizing NLP.
Beware of Data Bias
Typical Natural Language Processing (NLP) models learn based on their training data. Therefore, if the data used contains biases, there is a risk that the model will inherit those prejudices or make incorrect judgments.
A concrete example could be a case where the model reflects biases related to specific genders or regions. To prevent this issue, it is crucial to select diverse data and regularly evaluate and correct the model.
Understand the Model’s Scope of Application
While Natural Language Processing (NLP) is an excellent technology, it may not produce accurate results for tasks that fall outside its scope of application. For instance, general-purpose models might not fully comprehend legal documents or medical data rich in specialized terminology.
Therefore, when utilizing NLP, it is important to check the model’s application scope beforehand. If you need to perform tasks beyond its scope, creating a custom model suited to that specific content can maximize the effectiveness of AI implementation.
Consider Privacy and Security
Since Natural Language Processing (NLP) may process data containing personal information, it is essential to consider privacy and security. For example, when chatbots or voice assistants handle personal data, there are risks of inappropriate use or information leaks. To maintain user trust, it is important to thoroughly implement measures such as data anonymization, encryption, and clearly defining the purpose of data usage.

Conclusion
In this article, we explained the overview, mechanisms, capabilities, and application examples of Natural Language Processing (NLP).
By utilizing NLP, companies can leverage it in various business scenarios, such as conversational AI and text mining. Re-read this article to solidify your understanding of the mechanisms and representative use cases of NLP.
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March 2, 2026
What is a neural network? Explaining its relationship with machine learning and deep learning, and examples of its use.
In recent years, AI has been utilized in various scenes of daily life, and one of the core technologies behind it is the neural network. By understanding neural networks, we can gain a deeper insight into the potential and application scope of AI.
This article provides a comprehensive explanation of the mechanism of neural networks, their relationship with machine learning and deep learning, and examples of their use. If you are considering implementing AI in your company, please read through to the end.
What is a Neural Network?
A neural network is a type of AI technology designed to mimic the neural circuitry of the human brain. Computational units called nodes are arranged in layers, transmitting information to each other to perform complex processing.
This enables AI to learn patterns from vast amounts of data and perform advanced tasks such as image recognition and voice recognition. While it was difficult for conventional programs to process ambiguous data or make predictions, the advent of neural networks has made these tasks efficient.
Recently, neural networks have been utilized in various fields such as medicine, finance, and entertainment, attracting attention as a technology that greatly expands the possibilities of AI.

The Mechanism of a Neural Network
The mechanism of a neural network consists of three elements:
- Input
- Propagation
- Output
First, data is fed into the input layer, where information such as images or numerical values is represented in numerical form. Subsequently, propagation occurs through intermediate layers called hidden layers, where each node performs weighted calculations.
The results of these calculations are then transmitted to the next layer, and finally, the output layer generates a result. For example, in image recognition, it outputs a judgment such as “Is this image a dog or a cat?”
Through these processes, the neural network learns patterns in data and becomes capable of handling complex tasks. Neural networks are a crucial point in AI utilization, so it’s important to understand the general flow of how they work.

Relationship with Machine Learning and Deep Learning
Terms often confused with neural networks include machine learning and deep learning. Let’s take a closer look at the relationship between each.
Neural Networks and Machine Learning
Machine learning is a fundamental AI technology that learns patterns from data to make predictions and classifications, and it is used as a term referring to algorithms in general. On the other hand, a neural network is a specific method within machine learning, possessing a structure designed to mimic the neural circuits of the human brain.
In other words, a neural network can be considered one of the tools within machine learning. While machine learning includes a wide range of methods, from simple linear models to complex non-linear models, neural networks are particularly well-suited for large-scale data and complex tasks.
Thus, if machine learning is the broad framework of AI, then neural networks represent a specialized, advanced approach within it. Both machine learning and neural networks are AI technologies, but they are strictly distinguished as different concepts, so it’s important to understand the difference.
Neural Networks and Deep Learning
Deep learning can be described as a further evolution of neural networks. Specifically, deep learning is a technology that uses neural networks with many layers to enable learning of more complex and advanced patterns.
This multi-layered structure has enabled deep learning to achieve dramatic progress in fields such as image recognition, voice recognition, and natural language processing. In other words, deep learning is a deep version of neural networks, built upon the foundation of the neural network framework.
Understanding this relationship—where neural networks form the basis of deep learning, and deep learning expands its applications—will allow you to grasp their respective roles more clearly.

Learning Patterns of Neural Networks
How does learning proceed in a neural network? This chapter explains the learning patterns of neural networks by dividing them into two types.
Supervised Learning
Supervised learning is the most common method among neural network learning patterns.
Specifically, it involves giving the model pairs of input data and correct labels (answers), training it to predict the correct output. For example, by showing a photo and telling the AI “this is a dog,” the model learns to judge “this is a dog” when it sees a new photo later.
Here are examples of supervised learning applications:
- Image recognition: Identifying animals like cats and dogs, or objects like cars and buildings.
- Voice recognition: Analyzing human voices and converting them into text.
- Medical diagnosis: Detecting lesions from X-ray images.
A major appeal of supervised learning is its ability to achieve high accuracy in tasks where the correct answer is predetermined. However, it’s important to note that it requires the cost of preparing labeled data.
Unsupervised Learning
Unsupervised learning is a learning method used when correct labels do not exist.
In unsupervised learning, the model is given a large amount of data and learns to autonomously find the structure and patterns within that data. Analogous to human learning, it’s similar to “inquiry-based learning” where one grasps characteristics by organizing data on their own, without explicit explanations.
Here are examples of unsupervised learning applications:
- Clustering: Grouping customers based on purchasing tendencies.
- Anomaly detection: Finding outliers that deviate from normal data.
- Dimensionality reduction: Simplifying high-dimensional data.
A major advantage of unsupervised learning is its ability to utilize vast amounts of unlabeled data. It proves especially powerful in fields where data labeling is difficult or prior knowledge is limited.
Thus, supervised learning and unsupervised learning are approaches for different types of problems. Therefore, it is important to choose the optimal learning method according to your company’s objectives and the characteristics of your data.

Representative Types of Neural Networks
Did you know that even though we simply say “neural network,” there are actually various types? This chapter introduces representative types of neural networks.
CNN (Convolutional Neural Network)
CNN, also called a “convolutional neural network,” is a type of neural network specialized for image processing. It primarily extracts features from image data and performs classification and recognition based on those features.
A key characteristic of CNN is the mechanism called the convolutional layer. The convolutional layer plays the role of extracting features from image data, capturing pixel information locally to extract edges, patterns, and more.
Typical use cases for CNN include object recognition (classifying cats, dogs, etc.), autonomous driving (recognizing road signs and lanes), and the medical field (detecting lesions from MRI images). As such, CNN is widely used as an indispensable technology in projects handling image data.
RNN (Recurrent Neural Network)
RNN, also called a “recurrent neural network,” is a model specialized for learning the order of data or temporal sequences. A major feature of RNN is its recurrent structure, which allows it to memorize past information while making subsequent predictions.
This mechanism enables RNN to efficiently handle time-series data or sequence data. However, when learning long sequences, the vanishing gradient problem can occur, so caution is needed.
The vanishing gradient problem refers to an issue where, during the neural network learning process, the gradient (differential value) becomes extremely small when propagating errors to update weights, causing learning to stall as layers become deeper.
Typical use cases for RNN include voice recognition (analyzing continuous sounds), translation (generating sentences considering word order), stock price prediction, and weather data analysis. Thus, RNN is an effective option for tasks that involve a temporal dimension.
LSTM (Long Short-Term Memory)
LSTM is an extended model developed to overcome the vanishing gradient problem, a weakness of RNNs. As mentioned, the vanishing gradient problem is a phenomenon where the gradient carrying the error during learning gradually diminishes as it propagates back through layers, preventing deep layers from learning and hindering model accuracy.
LSTM maintains an internal memory cell, allowing it to retain information over long periods while discarding unnecessary information as needed. This enables efficient learning even with long sequence data. A key feature of LSTM is its ability to handle more advanced tasks, such as generating text while considering the overall context of a sentence.
Typical use cases for LSTM include natural language processing (chatbots, text generation), music generation (composing considering melodic structure), and biometric data analysis (analyzing heart rate or brainwave patterns). Thus, LSTM is effective for tasks that require capturing long-term dependencies.
Autoencoder
An autoencoder is a neural network specialized for compressing input data and then reconstructing new data based on that compressed representation. Autoencoders are often used primarily to extract essential features of data and are positioned as a type of unsupervised learning.
A typical autoencoder consists of two components: an “encoder” and a “decoder.” The encoder transforms data into a latent space, and the decoder recreates the input based on that latent representation. A major characteristic of autoencoders is their ability to utilize this mechanism for tasks like data denoising and anomaly detection.
Use cases for autoencoders include data compression (efficient storage of image data), noise removal (restoring old photos or audio), and anomaly detection (detecting patterns deviating from normal data). Thus, using an autoencoder is a good approach when you want to grasp the characteristics of your data.
We have introduced four types of neural networks here, but their features and areas of expertise are diverse. Therefore, by appropriately choosing the type of neural network according to the application, you can maximize the effectiveness of AI utilization.

Points to Consider When Using Neural Networks
While neural networks are indispensable for AI utilization, there are several key points to be aware of when using them. This chapter introduces three important considerations.
Ensure Data Quality and Quantity
Neural networks achieve high accuracy by learning patterns from large amounts of data. However, if data is insufficient or contains significant noise, model performance will degrade substantially. Therefore, it is crucial to thoroughly preprocess and clean the data to ensure appropriate quantity and quality.
Implement Measures to Prevent Overfitting
A major characteristic of neural networks is their high flexibility. This flexibility can lead to “overfitting,” where the model fits the training data too closely, resulting in poor generalization performance on unseen data. To prevent this problem, techniques like dropout and regularization methods should be employed, and an appropriate model size must be chosen.
Consider Computational Costs and Resources
Training neural networks requires significant computational resources, and deep models, in particular, demand substantial time and cost. For efficient model development, careful consideration must be given to selecting the hardware environment (GPUs, TPUs) and potentially implementing distributed learning. It is important to plan proactively to avoid project setbacks due to budget or resource constraints.

Conclusion
This article has explained the mechanism of neural networks, their relationship with machine learning and deep learning, and examples of their application.
By utilizing neural networks, companies can leverage them in various business scenarios. Revisit this article to solidify your understanding of the mechanisms and representative types of neural networks.

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March 2, 2026
How much is the annual income of a machine learning engineer? Introducing annual income situation and salary increase method
Machine learning is used in many places and is one of the technologies that enrich our lives. Along with that, attention is also focused on machine learning engineers who build machine learning. What I am concerned about is the outline of machine learning engineers and the annual income situation. This time, we will introduce the outline of machine learning engineers and the annual income situation.

What kind of profession is a machine learning engineer?

Due to the rapid development of AI technology and the promotion of digital transformation by companies, the attention of machine learning engineers is increasing. So, first, I will explain about machine learning engineers.
What is a machine learning engineer?
A machine learning engineer is a job that specializes in machine learning technology in AI development. Machine learning is one of the technologies required to realize AI, and derives certain rules and rules based on a large amount of data. Machine learning engineers use their knowledge of statistics and mathematics to build machine learning models in order to find certain regularities from large amounts of data.

Job description of a machine learning engineer
Machine learning engineers are mainly responsible for data analysis, construction, and construction and verification of machine learning models. In particular, building and verifying machine learning models is the most important task. In machine learning, the accumulated data is learned according to the constructed machine learning model. At this time, the learning result makes a big difference depending on the machine learning model selected. Therefore, it is necessary to modify the machine learning model with higher accuracy by repeating the verification.

Differences between AI engineers and data scientists
Similar occupations to machine learning engineers include AI engineers and data scientists. Since AI engineer is a term that refers to all engineers involved in AI, machine learning engineers are also included in AI engineers. However, some companies make a clear distinction between AI engineers and machine learning engineers. A data scientist is a job that is treated in much the same way as a machine learning engineer when looking at the work content. However, some companies make a clear distinction, so it is necessary to confirm in advance.

Check the annual income of machine learning engineers

Next, let’s take a look at the annual income of machine learning engineers.
Domestic average annual income
According to data from the job search site Indeed, the average annual income of machine learning engineers in Japan is around 6.3 million yen. According to the site, the average annual salary of a general engineer is about 4-5 million yen, so it can be said that machine learning engineers have a high salary level. In recent years, the Ministry of Economy, Trade and Industry has been promoting digital transformation, and the importance of AI is increasing in society as a whole. Therefore, increasing demand is increasing the number of companies offering higher salaries.

Trends in average annual income overseas
Overseas machine learning engineers are often offered higher annual salaries than domestic ones. According to the data of the American recruiting site “Glassdoor”, the average annual income of machine learning engineers is about 14 million yen. If you are a machine learning engineer and want to achieve a high annual income, it is recommended that you also consider working overseas. However, the level required is likely to be higher than in Japan, so you must improve your skills on a daily basis.

To aim to increase annual income as a machine learning engineer

In order to increase your annual income as a machine learning engineer, the following points are important.
- Catch up on the latest technology
- Learn a programming language
- Improve project management skills
- Improve your English
- Consider changing jobs
Catch up on the latest technology
As technology advances rapidly in the IT industry, it is important to keep an antenna on and catch up with the latest technology and knowledge. In particular, new information is being transmitted one after another in advanced technologies such as machine learning and AI. There are few human resources who can handle the latest technology and the demand is high, so if you do not miss the catch-up, you will be a valuable human resource.

Learn a programming language
In order to realize machine learning, it is necessary to code “Python” and “R language”. Therefore, you must acquire the knowledge to code each one. Also, when implementing machine learning, knowledge of specialized libraries and frameworks is also required, so it is good to remember the commonly used “NumPy, Pandas, Tensorflow, Matplotlib” and so on.

Improve project management skills
If you want to advance your career as an engineer, it is important to improve your project management skills in addition to technology skills. If you gain some experience as an engineer, you will have more opportunities to play an active role not only in the field but also as a team leader. In addition to managing the team, there is a wide range of tasks such as formulating development policies and selecting technologies for solving problems. Managers tend to have higher annual incomes, so think ahead and improve your management skills.

Improve your English
The fields of machine learning and AI are more advanced overseas than in Japan. Therefore, you have to read foreign documents to get the latest knowledge. At that time, it is important to acquire English proficiency so that you can read overseas documents. By acquiring the latest information, even domestic companies will be able to become valuable human resources.

Consider changing jobs
After gaining experience as a machine learning engineer, it is a good idea to move to a company with better conditions. Due to the high demand for machine learning engineers themselves, it is possible that you will find a company that will generate a higher annual income for the same job. Furthermore, if you are confident in your language skills such as English, it is also effective to work overseas such as the United States in search of a high income.

To get a satisfying annual income with a machine learning engineer

The following points are important for a machine learning engineer to earn a satisfactory annual income.
- Choose a job-based employment company
- Choose a company that is willing to invest in education
- Check the personnel evaluation system
Choose a job-based employment company
Job-type employment is employment that emphasizes external competitiveness and determines salary based on market value. Since job-based employment focuses on skills and determines salary, it is likely that the salary will be higher than the seniority-based assessment found in many companies.

Choose a company that is willing to invest in education
It is important for engineers to constantly study and improve their skills. Therefore, by selecting a company with a well-educated environment, you can work while increasing your own market value. Also, a company that is willing to invest in education is a company that understands the value of engineers, so it is highly likely that it will get a proper evaluation. By increasing the market value, you can expect high annual income even when you change jobs.

Check the personnel evaluation system
In order to obtain a satisfactory annual income, it is important to have an evaluation system that clearly and properly evaluates your contribution. Many companies often carry out personnel evaluations in seniority order. However, in seniority, there is almost no such thing as a sudden promotion or salary increase due to evaluation. Therefore, it is important that the evaluation system only properly evaluates ability.

If you want to touch machine learning, leave it to TRYING
To become a machine learning engineer, you must acquire knowledge and skills related to machine learning. However, it is difficult to learn machine learning from scratch. Therefore, I would like to recommend two cloud-based business efficiency improvement tools developed by TRYING.

Experience machine learning with the no-code AI tool “UMWELT.”
UMWELT is a tool that can use AI without programming. There is no need to prepare a special environment for using AI in-house, and AI can be used while minimizing preparation costs such as costs and man-hours. It is equipped with a large number of AI algorithms that help improve work efficiency, and anyone can easily build an AI system by freely combining these algorithms.

Easy automatic shift creation with AI cloud “HR BEST”
HRBEST is a tool that automates complex shift creation with “combinatorial optimization” technology. Since shifts can be created according to the Labor Standards Law and industry rules, shift creation work for managers, who tend to be personalized, can be easily automated. Since it is a cloud service, it is always possible to respond to sudden shift changes and law revisions, and always keep the latest shift status.

summary
Machine learning engineers have a higher average annual income than general engineers, and it is expected that the level will continue to rise in the future. Many companies would like to acquire machine learning engineers to improve work efficiency and create businesses. However, it is difficult to hire machine learning engineers, and many companies may be worried that they will not be able to incorporate machine learning. UMWELT / HRBEST is an AI tool that can be used without the need for an instrument learning engineer. Please take this article as an opportunity to contact TRYING.

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December 5, 2025
How much is the annual income of a machine learning engineer? Introducing annual income situation and salary increase method
Machine learning is used in many places and is one of the technologies that enrich our lives. Along with that, attention is also focused on machine learning engineers who build machine learning. What I am concerned about is the outline of machine learning engineers and the annual income situation. This time, we will introduce the outline of machine learning engineers and the annual income situation.

What kind of profession is a machine learning engineer?

Due to the rapid development of AI technology and the promotion of digital transformation by companies, the attention of machine learning engineers is increasing. So, first, I will explain about machine learning engineers.
What is a machine learning engineer?
A machine learning engineer is a job that specializes in machine learning technology in AI development. Machine learning is one of the technologies required to realize AI, and derives certain rules and rules based on a large amount of data. Machine learning engineers use their knowledge of statistics and mathematics to build machine learning models in order to find certain regularities from large amounts of data.

Job description of a machine learning engineer
Machine learning engineers are mainly responsible for data analysis, construction, and construction and verification of machine learning models. In particular, building and verifying machine learning models is the most important task. In machine learning, the accumulated data is learned according to the constructed machine learning model. At this time, the learning result makes a big difference depending on the machine learning model selected. Therefore, it is necessary to modify the machine learning model with higher accuracy by repeating the verification.

Differences between AI engineers and data scientists
Similar occupations to machine learning engineers include AI engineers and data scientists. Since AI engineer is a term that refers to all engineers involved in AI, machine learning engineers are also included in AI engineers. However, some companies make a clear distinction between AI engineers and machine learning engineers. A data scientist is a job that is treated in much the same way as a machine learning engineer when looking at the work content. However, some companies make a clear distinction, so it is necessary to confirm in advance.

Check the annual income of machine learning engineers

Next, let’s take a look at the annual income of machine learning engineers.
Domestic average annual income
According to data from the job search site Indeed, the average annual income of machine learning engineers in Japan is around 6.3 million yen. According to the site, the average annual salary of a general engineer is about 4-5 million yen, so it can be said that machine learning engineers have a high salary level. In recent years, the Ministry of Economy, Trade and Industry has been promoting digital transformation, and the importance of AI is increasing in society as a whole. Therefore, increasing demand is increasing the number of companies offering higher salaries.

Trends in average annual income overseas
Overseas machine learning engineers are often offered higher annual salaries than domestic ones. According to the data of the American recruiting site “Glassdoor”, the average annual income of machine learning engineers is about 14 million yen. If you are a machine learning engineer and want to achieve a high annual income, it is recommended that you also consider working overseas. However, the level required is likely to be higher than in Japan, so you must improve your skills on a daily basis.

To aim to increase annual income as a machine learning engineer

In order to increase your annual income as a machine learning engineer, the following points are important.
- Catch up on the latest technology
- Learn a programming language
- Improve project management skills
- Improve your English
- Consider changing jobs
Catch up on the latest technology
As technology advances rapidly in the IT industry, it is important to keep an antenna on and catch up with the latest technology and knowledge. In particular, new information is being transmitted one after another in advanced technologies such as machine learning and AI. There are few human resources who can handle the latest technology and the demand is high, so if you do not miss the catch-up, you will be a valuable human resource.

Learn a programming language
In order to realize machine learning, it is necessary to code “Python” and “R language”. Therefore, you must acquire the knowledge to code each one. Also, when implementing machine learning, knowledge of specialized libraries and frameworks is also required, so it is good to remember the commonly used “NumPy, Pandas, Tensorflow, Matplotlib” and so on.

Improve project management skills
If you want to advance your career as an engineer, it is important to improve your project management skills in addition to technology skills. If you gain some experience as an engineer, you will have more opportunities to play an active role not only in the field but also as a team leader. In addition to managing the team, there is a wide range of tasks such as formulating development policies and selecting technologies for solving problems. Managers tend to have higher annual incomes, so think ahead and improve your management skills.

Improve your English
The fields of machine learning and AI are more advanced overseas than in Japan. Therefore, you have to read foreign documents to get the latest knowledge. At that time, it is important to acquire English proficiency so that you can read overseas documents. By acquiring the latest information, even domestic companies will be able to become valuable human resources.

Consider changing jobs
After gaining experience as a machine learning engineer, it is a good idea to move to a company with better conditions. Due to the high demand for machine learning engineers themselves, it is possible that you will find a company that will generate a higher annual income for the same job. Furthermore, if you are confident in your language skills such as English, it is also effective to work overseas such as the United States in search of a high income.

To get a satisfying annual income with a machine learning engineer

The following points are important for a machine learning engineer to earn a satisfactory annual income.
- Choose a job-based employment company
- Choose a company that is willing to invest in education
- Check the personnel evaluation system
Choose a job-based employment company
Job-type employment is employment that emphasizes external competitiveness and determines salary based on market value. Since job-based employment focuses on skills and determines salary, it is likely that the salary will be higher than the seniority-based assessment found in many companies.

Choose a company that is willing to invest in education
It is important for engineers to constantly study and improve their skills. Therefore, by selecting a company with a well-educated environment, you can work while increasing your own market value. Also, a company that is willing to invest in education is a company that understands the value of engineers, so it is highly likely that it will get a proper evaluation. By increasing the market value, you can expect high annual income even when you change jobs.

Check the personnel evaluation system
In order to obtain a satisfactory annual income, it is important to have an evaluation system that clearly and properly evaluates your contribution. Many companies often carry out personnel evaluations in seniority order. However, in seniority, there is almost no such thing as a sudden promotion or salary increase due to evaluation. Therefore, it is important that the evaluation system only properly evaluates ability.

If you want to touch machine learning, leave it to TRYING
To become a machine learning engineer, you must acquire knowledge and skills related to machine learning. However, it is difficult to learn machine learning from scratch. Therefore, I would like to recommend two cloud-based business efficiency improvement tools developed by TRYING.

Experience machine learning with the no-code AI tool “UMWELT.”
UMWELT is a tool that can use AI without programming. There is no need to prepare a special environment for using AI in-house, and AI can be used while minimizing preparation costs such as costs and man-hours. It is equipped with a large number of AI algorithms that help improve work efficiency, and anyone can easily build an AI system by freely combining these algorithms.

Easy automatic shift creation with AI cloud “HR BEST”
HRBEST is a tool that automates complex shift creation with “combinatorial optimization” technology. Since shifts can be created according to the Labor Standards Law and industry rules, shift creation work for managers, who tend to be personalized, can be easily automated. Since it is a cloud service, it is always possible to respond to sudden shift changes and law revisions, and always keep the latest shift status.

summary
Machine learning engineers have a higher average annual income than general engineers, and it is expected that the level will continue to rise in the future. Many companies would like to acquire machine learning engineers to improve work efficiency and create businesses. However, it is difficult to hire machine learning engineers, and many companies may be worried that they will not be able to incorporate machine learning. UMWELT / HRBEST is an AI tool that can be used without the need for an instrument learning engineer. Please take this article as an opportunity to contact TRYING.

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December 5, 2025

3 Recommended PCs for machine learning / deep learning | Explain the necessary parts!

What is machine learning

Machine learning is one of the most important aspects of AI development, and is an effective method for predicting numerical values and identifying and classifying images.

One of the machine learning methods is ” neural network . A neural network is a technique inspired by the structure of the human brain and mimics the way neurons work.

Some of the neural networks include multi-layer perceptrons and deep learning.

What is deep learning?

Deep learning was developed to enhance the capabilities of the neural network mentioned in the previous section.

Deep learning is a neural network with a multi-layered structure, and is currently the mainstream of AI development.

The difference between machine learning and deep learning is that machine learning learns rules from data by itself. In deep learning, the computer itself learns the feature values that must be specified in machine learning.

To put it simply, machine learning involves specifying feature values by humans, and deep learning involves learning feature values as well.

Currently, deep learning is used for image recognition , speech processing, natural language processing , etc. a variety of situations around the world, including

From here, let’s see what kind of PC should be used when actually performing deep learning.

Parts required for deep learning PC

The following five parts are required for a PC for deep learning.

OS
CPU
GPUs
Memory
Storage

I will explain each.

OS

OS is an abbreviation of “Operating system”, and refers to the system software that controls the operation (operation, operation, and operation) of a computer. In terms of a PC, it is a system that connects the device and software of the PC and controls the device and software.

Major PC OSs include Microsoft’s Windows, Apple’s mac OS, and open source Linux.

In AI development, Windows is recommended because it is easy to expand functions, and Linux is also used for servers. For Windows, the pro series is better from the point of view of functions

CPU

CPU is an abbreviation for “Central Processing Unit” and is the central processing unit in a computer (the brain of the computer, so to speak). CPUs are versatile in their processing and can handle a variety of things.

When choosing a PC for deep learning, it is a good idea to choose a higher model from Intel’s CPU “core i5”.

GPUs

GPU is an abbreviation for “Graphics Processing Unit”, and is a computing device specialized for screen display and image processing such as 3D graphics.

GPUs are good at simple calculations and good at parallel processing, so they are a very important part in AI development.

GPU processing speed is several to 100 times faster than CPU processing speed, and GPU is essential for deep learning.

Memory

Memory is the temporary storage of your computer’s work. Since it is temporary, it is characterized by fast access so that the current contents can be retrieved immediately.

When choosing a PC for deep learning, it is a good idea to choose a memory of 16GB or more.

Storage

Storage, also known as “auxiliary storage”, stores data for a long period of time. What is called a hard disk or SSD is one of this storage.

There is no problem with the storage that is installed in a normal PC, but if it is 512 GB or more, it can handle large amounts of data, so you can rest assured.

Differences between deep learning PCs and ordinary PCs

There are three differences between deep learning PCs and ordinary PCs: differences in specifications for each part,''using Linux as the OS,” and “requiring a GPU.”

In addition, PCs for deep learning are a type of workstation, and feature higher performance than regular PCs.

Also, some people who are serious about deep learning development use a PC that they have assembled with the necessary parts themselves.

In the following, I will briefly introduce “ordinary PC”, “self-made PC”, and “workstation/deep learning PC” as a supplement.

Normal PC

Deep learning can be performed even on a PC that is normally sold if it is equipped with a GPU.

For those who are studying deep learning for the first time or who want to try deep learning, a normal PC may be fine.

Homemade PC

If you want to do full-scale AI development, you should use your own PC. We also recommend the BTO PC, which allows you to select parts to some extent.

BTO: An abbreviation for “Build To Order”, which means build-to-order manufacturing. Compared to commercially available finished PCs, you can freely customize the processor, memory, hard disk, mouse, storage, etc.

Workstation/PC for deep learning

Workstations are used by individuals for work such as CAD. If you find it difficult, remember that it is a version with good performance on a normal PC.

* CAD: Design support software for automobiles, architecture, and clothing.

Supplement: Server

In addition to the above three methods, there are other ways to develop on the server. A server is used by many users. For personal use, you should choose one of the above three options.

Should I make my own PC for deep learning?

Earlier, I mentioned that “Some people who are serious about deep learning development use their own PCs.”

Below, we will introduce the advantages and cautions of using a self-made PC for those who are wondering whether they should build their own PC for deep learning.

Advantages of using your own PC

The advantage of using a self-made PC is that it can be specialized for deep learning and machine learning.

Homemade PCs can be assembled to have higher specs than those sold at regular stores, so it is recommended when a server cannot be used.

Points to note when making your own

One thing to keep in mind when building your own PC for deep learning is that you cannot request guarantees or repairs from the sales company.

It goes without saying that you build your own PC, but basically if something goes wrong, you have to investigate and deal with it yourself, or pay a certain amount of money and ask for a PC repair.

Therefore, if you are not very familiar with PCs and machines, you need to be careful when building your own PC.

3 Recommended PCs for Deep Learning

From here, we will introduce recommended PCs for deep learning. The following three PCs are introduced this time.

DEEP-17FG102-i7K-VOXVI
THIRDWAVE Pro WORKSTATION X4612 standard model
HP ZBook Studio 15.6inch G8 Mobile Workstation new standard model

①DEEP-17FG102-i7K-VOXVI

machine learning

The first recommended PC is “DEEP-17FG102-i7K-VOXVI”.

OS	Ubuntu 18.04 LTS
CPU	Core i7-9700K Intel Z370
memory	DDR4-2400 SODIMM (PC4-19200) 16GB (8GB x 2)
Storage ①	250GB NVMe M.2 SSD
Storage②	1TB Serial-ATA HDD
drive	No optical drive
GPUs	GeForce RTX 2080 8GB GDDR6
display	17.3 type (matte color liquid crystal) full HD (1920 x 1080 dots)
price	32,3980 yen ~ (as of 2022/02/08)

It supports 8GB of high-speed GDDR6 memory similar to desktop and GPU Boost 4.0 that brings out GPU performance, so code created at the development site can be executed at a speed comparable to mobile environments.

Although this PC is a notebook PC, it boasts performance comparable to that of a desktop PC. It is one that can be used at the forefront of AI development, such as creating sample code, demonstrating, and giving presentations.

In addition, the same PC comes with ax Co., Ltd.’s demo software “ailia AI showcase”, so you can use various AI functions using trained models such as object detection, image classification, feature extraction, skeleton extraction, and personal identification. You can easily try it.

In addition, it supports the GPU Cloud platform “NGC (NVIDIA GPU Cloud)” that facilitates AI development, and the latest development environment can be used without complicated settings.

Just by downloading the deep learning framework, you can use it without worrying about complicated environment settings and consistency, so it is the best laptop for those who are just starting deep learning.

A framework is a piece of software that serves as the foundation upon which an application is developed.

②THIRDWAVE Pro WORKSTATION X4612 standard model

The second recommended PC is “THIRDWAVE Pro WORKSTATION X4612 standard model”.

OS	none
CPU	Intel Xeon Silver 4210R (rated 2.40GHz/3.20GHz/13.75MB/10Core/20Thread at TB) x2
memory	32GB (DDR4-3200 ECC RDIMM/16GB×2)
storage	No disc (2.5″ rear bay 1)
GPUs	NVIDIA T600 4GB (MiniDisplayPort x4) x 1 [Order]
price	Price starts at 72,8860 yen (as of 02/08/2022)

The THIRDWAVE Pro WORKSTATION X4612 standard model is a high-end model that achieves expandability and powerful performance. *Since there is no OS, you will have to choose by yourself.

Up to two NVIDIA® Quadro® and NVIDIA® GeForce® series ultra-high-end graphics cards can be installed.

In addition, assuming use on the desk side, it can be operated with a commercial 100V power supply, and can be used for various purposes such as high-resolution video/audio editing, deep learning , CAE/CAD, and 3D animation. .

③HP ZBook Studio 15.6inch G8 Mobile Workstation New standard model

The third recommended PC is “HP ZBook Studio 15.6inch G8 Mobile Workstation New Standard Model”.

OS	Windows 10 Pro (Japanese) (Downgrade from Windows 11 Pro)
CPU	Intel® Core™ i7-11800H processor (max frequency 4.6GHz, 8 cores/16 threads, 24MB cache)
memory	16GB DDR4-3200 (onboard)
storage	512GB M.2 SSD (PCIe, NVMe, SED OPAL2, TLC)
GPUs	Intel® UHD Graphics and NVIDIA T1200 (4 GB GDDR6)
display	15.6 inch wide full HD liquid crystal display (matte panel, maximum resolution 1920 x 1080, maximum brightness 400cd/m², maximum 16.77 million colors, IPS method, LED backlight, ambient light sensor)
price	Price starts from 35,2000 yen (as of 02/08/2022)

The next-generation Intel® Core™ i9 vPro® processor in the PC is designed to handle complex multi-threaded applications, making multitasking easy.

The HP ZBook Studio 15.6inch G8 Mobile Workstation New Standard Model is a laptop designed for performance workflows in every aspect, from keyboard to screen.

Also, up to NVIDIA RTX™ A5000 or GeForce RTX™ 3080 GPUs can be installed. So you can seamlessly render, design and multitask even with heavy files.

And with NVIDIA RTX™ professional graphics, the PC can query millions of rows of data sets and analyze them in real time, making it the perfect PC for data scientists and business intelligence professionals.

Summary

How was it?

This time, I explained what deep learning is and the difference between a PC for deep learning and a normal PC.

A PC has various parts and I think it is difficult, but I would like you to acquire knowledge by all means.

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June 10, 2024