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What is Cloud AI?

Due to improvements in database technology and network technology, the use of cloud AI, which processes AI in the cloud, is progressing.

With further technological innovations such as the development of 5G, the role played by cloud AI will become even greater.

Recently, an increasing number of companies are recognizing the importance of data and promoting data utilization, and the cloud plays a major role in the processing of such data and its processing speed.

This time, we will explain the advantages and disadvantages of cloud AI and the situations where it can be actually introduced and utilized with examples. And I will also explain the difference from edge AI, which has been a hot topic recently.


  • What is cloud AI?
  • Three benefits of cloud AI
    • No load on your server
    • Complex and advanced processing possible
    • Easy management such as application of learned data
  • 3 Disadvantages of Cloud AI
    • Sending and receiving huge amounts of data lacks real-time performance
    • Risk of information leakage
    • As the amount of data increases, the amount of communication increases
  • Differences between cloud AI and edge AI
    • Disadvantages of edge AI and recent technological developments
  • 3 AI cloud services
    • ①Google Cloud Platform
    • (2) Amazon Web Services
    • ③Microsoft Azure
  • 3 Case Studies of Cloud AI Introduction
    • (1) Utilization of “XaaS (X as a service)”
    • ②Material informatics “TABRASA”
    • ③ Transcription service “Mojiko”
  • in conclusion

What is cloud AI?

Starting with Gmail, the cloud is something that many people are familiar with. The name cloud originated in 2006 when then-Google CEO Eric Schmidt called the service “cloud computing.”

In the latter half of the 1990s, Internet use spread rapidly as computer prices began to fall in the Internet age. At that time, many companies were overwhelmed with internet speeds, applications and data, and server sprawl. In addition, it was difficult to handle such a server because it was necessary to rent or prepare it yourself, and it was necessary to design the capacity in advance.

In the 2000s, the cloud was the solution to this problem. This technology has made it possible to easily use necessary services with software and data via the Internet.

Furthermore, the cloud is indispensable for IoT technology, which is a hot topic these days. For example, services are being developed that allow users to operate and manage home appliances from their smartphones while away from home. The amount of data sent by these services is extremely large ( big data ), and cannot be covered by the servers owned by one company. Cloud services are used for this purpose. In addition to being able to expand freely, the cloud eliminates the need for server management and keeps costs low, so it can greatly reduce the burden on companies.

Looking at the background to the birth of the cloud and its current use, I think you can understand why AI is being used in the cloud.

Three benefits of cloud AI

The benefits of using cloud AI are:

  • No load on your server
  • Complex and advanced processing possible
  • Easy management such as application of learned data

I will explain in detail from now on.

No load on your server

Cloud AI is a mechanism that performs AI learning and data processing on the cloud, so there is no need to perform complex information processing on your own server or terminal, which can reduce the load.

The computer that processes information on the cloud is also provided by the data server, so it can be said that there is no need to prepare a high-performance computer in-house.

Complex and advanced processing possible

Cloud AI accumulates data on a large-scale server and processes and analyzes the data, making it possible to perform complex and advanced processing.

The great strength of cloud AI is that it can process a huge amount of information that cannot be processed by AI installed in small terminals such as personal computers.

Easy management such as application of learned data

One of the challenges in introducing AI is the preparation of data for learning.

When using cloud AI, learning data is prepared in advance on the cloud, and highly accurate AI that has learned from that data can be used.

Since there is no need to prepare highly reliable data or perform installation work such as building a learning model, AI can be used without advanced knowledge of AI.

3 Disadvantages of Cloud AI

Disadvantages of using cloud AI are as follows.

  • When sending and receiving a large amount of data, processing via the Internet lacks real-time performance.
  • Risk of information leakage
  • As the amount of data increases, the amount of communication increases

I will explain in detail from now on.

Sending and receiving huge amounts of data lacks real-time performance

Cloud AI sends and receives information from the terminal at hand to the cloud and processes information on the cloud.

If you need to send or receive a large amount of data, you may exceed your data bandwidth and experience delays.

However, in recent years, the use of 5G lines, which are capable of transmitting and receiving more data, is increasing, so it is expected that cloud AI will be able to be used in real time in the future.

Risk of information leakage

When using cloud AI, all information must be sent to the cloud via the Internet, increasing the risk of information leakage.

There is also a risk of information leakage while information is stored on the cloud.

It can be said that confidential information such as internal secrets is not suitable for processing with cloud AI.

As the amount of data increases, the amount of communication increases

Internet connection is used to send data from the terminal to the cloud.

As the amount of data increases, the amount of communication increases, which not only increases the risk of delays but also increases communication costs.

There are costs that can be cut by using cloud AI, such as not having to manage AI in-house, so it is necessary to consider what kind of service to use in consideration of other costs.

Differences between cloud AI and edge AI

In recent years, there is a technology called “edge AI”.

As the use of IoT is promoted and AI technology advances, the lack of real-time performance and privacy issues, which I mentioned earlier as the disadvantages of cloud AI, have come to the fore.

That’s where edge AI comes in. In cloud AI, data accumulation, learning, and inference were all performed on the cloud, but in edge AI, inference can be performed without using the cloud by incorporating a learning model into a terminal (edge ​​device).

Edge AI has the following features.

  • Real-time performance can be secured because the prediction is performed on the end device (edge ​​device)
  • Security is strong because prediction is performed without going through the Internet
  • Communication cost savings

Edge AI is attracting a great deal of attention, especially in research on industrial machinery and self-driving cars, as a technology that can successfully compensate for the disadvantages of cloud AI.

Disadvantages of edge AI and recent technological developments

However, Edge AI also has its disadvantages.

  • Incompetence of edge devices (compactness, power consumption in inference, etc.)
  • Advanced prediction is not possible because the environment for learning (cloud side) and the environment for inference (edge ​​device side) are (sometimes) different.
  • Security is not perfect as data is (may be) sent to the cloud to generate learning models

Due to the above problems, edge AI alone has been considered insufficient.

Therefore, in recent years, edge AI that combines high efficiency, high-speed processing, and small size has been developed, and there are great expectations for it as the IoT society advances.

Led by the New Energy and Industrial Technology Development Organization (NEDO), private companies and universities are developing computer technology and AI chips that can achieve both high speed and ultra-low power consumption.

Furthermore, edge AI startup AISing Inc. announced last December that it had developed an ultra-compact edge AI algorithm called “Memory Saving Tree (MST)” that can be placed on your fingertips.

This memory-saving “MST” is expected to be introduced in a wide variety of fields such as home appliances, smartwatches, and automobiles.

In this way, the development of edge AI is currently being actively carried out, and it is attracting attention as an important technology that supports the quaternary industry.

3 AI cloud services

Here are three AI cloud services.

  1. Google Cloud Platform
  2. Amazon Web Services
  3. Microsoft Azure

①Google Cloud Platform

Google Cloud Platform is an AI cloud service provided by Google, and it is a cloud service that implements various functions such as more than 20 free functions, paid functions, and business functions.

It is an easy-to-use service for those who are thinking of using the AI ​​cloud service for the first time, such as new users can receive usage rights for $ 300.

It provides a function to manage large amounts of data and a function to cluster images.

(2) Amazon Web Services

Amazon Web Services is an AI cloud service provided by Amazon, and is attractive for its diverse functions and rich free experience.

There are three types of free functions: free for a short period of time, free for one year, and unlimited, and there are over 100 services that you can try for free.

It is attractive that various services such as machine learning, log analysis, and relational database services can be used free of charge.

We also have extensive support for start-up companies.

③Microsoft Azure

Microsoft Azure is an AI cloud service provided by Microsoft, and you can experience popular services for free for 12 months.

After the free trial period ends, we will move to a pay-as-you-go system, but you can continue to use more than 40 services for free.

AI analyzes customer trends to build mobile experiences, supports the development of new apps, and efficiently manages websites.

3 Case Studies of Cloud AI Introduction

(1) Utilization of “XaaS (X as a service)”

Businesses using cloud AI are currently seen in various places, and among them, the need for cloud computing services such as ” SaaS “, “MaaS”, and ” PaaS ” has been increasing in recent years.

These various services are collectively called “XaaS”, and services are developed and operated by a wide range of companies, from large companies such as Google and Microsoft to venture companies.

This time, we will introduce the latest examples of SaaS (Software as a Service), which is attracting particular attention among XaaS.

②Material informatics “TABRASA”

The platform “TABRASA” is a materials informatics (MI) service ( SaaS ) jointly developed by NAGASE and IBM .

*MI (Materials Informatics): A technology that uses AI to improve the efficiency of research and development by chemists. A search algorithm that uses past material experiments and simulation data makes it possible to develop and commercialize new materials more quickly.

The feature of this service is that you can search for materials with two engines, “cognitive” and “analytics”.

“Analytics” is an approach to learn the chemical structural formula and physical property values ​​and derive the chemical structural formula of the substance desired by the user. On the other hand, “cognitive” is an approach that reads papers, patents, encyclopedias, experimental data, etc. related to materials into AI, systematizes them, and makes new guesses and proposals.

“Cognitive” is a highly customizable and unprecedented approach. The easy-to-understand and easy-to-operate UI makes it possible to use the service even if you are not a research specialist, so it can be applied to a wide range of fields.

③ Transcription service “Mojiko”

“Mojiko” is a “transcription editor” service that uses AI speech recognition technology developed by TBS TV.

In the TV and radio industry, a lot of “transcription” is done every day, but because it is a very laborious task, it is a heavy burden on the site of program production. In order to reduce the burden of such work as much as possible, TBS TV started developing “Mojiko”.

As a result, after materials such as audio and video files collected are automatically converted into text by the latest AI speech recognition engines of IT companies, human beings can immediately “correct and edit” sentences that are not correctly recognized. became.

Currently, TBS licenses “Mojiko” to Yoshizumi Information Co., Ltd. and sells it to general companies. Mojiko is expected to play a role in reforming the way people work in the media industry, where working hours are said to be long.

Even in industries such as the media industry, where IT and DX are slow to progress, starting with services that are easy to implement will make it easier for XaaS to take root, and it may gradually bring about positive changes in the way work is done.

in conclusion

In this article, we have explained a wide range of things, from the background of the creation of the cloud to the technological development and introduction of cloud AI. Did you understand that the existence of this cloud is indispensable for the utilization and development of AI?

If you follow popular XaaS services, you can see the current movement of the IT society. With the advent of the AI ​​era and the progress of the IoT society, there is a great possibility that cloud and cloud services will further advance in technological development and diversification of services. We will continue to pay attention to cloud technology, which will be the “unsung hero” that will create a new society.


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