Introduction points and concrete examples of machine learning. What should I be careful about?

Some may think that the introduction of machine learning is still a long way off, but machine learning is being applied in various fields. Recently, it has been introduced not only to large companies but also to small and medium-sized companies. What is the significance of introducing a machine learning system by a company?

What is the significance of working on machine learning?
There are various significances for companies to work on machine learning, such as acquiring new customers, increasing sales, and reducing risks, but by introducing machine learning, not only can you aim for operational efficiency and business growth, but you can also find business opportunities. Possibility is one of the big motivations.

It is the cost reduction part that has produced the most impact on general companies. Typical examples are shift efficiency systems that are expected to have an effect of 100 million units, and systems that apply machine learning to the automation of Web service monitoring that brings about significant cost reductions. In the Web service monitoring system, we were able to significantly reduce the labor cost for content check by letting the computer learn inappropriate images and automating the posted image confirmation work.

In systems such as RPA, which mainly focused on business automation, humans made “judgments.” However, by using it in combination with machine learning, high-precision work including “judgment” is realized. In the future, if machine learning systems make various business decisions other than business automation, an industrial revolution beyond business automation by introducing robots may occur.

How to introduce machine learning in-house?
There are three ways to introduce machine learning to your company:

Introducing ready-made package software and services
You can start machine learning by installing packaged software and services for machine learning such as Python and SQL. It has the advantage that it does not require any special know-how, and predictive analysis can be performed immediately. However, there are also drawbacks such as high installation cost and inability to customize.

Build your own system
If you use the method of building your own system, you can build the system that is most suitable for your company, such as selecting the optimum algorithm, developing programs, and tuning the system. However, it takes a considerable amount of time and effort to build a system, and if your company does not have a development department, you need to ask an external IT company or data analysis company to build the system.

Built using cloud services
The method of using cloud services requires human resources with knowledge of programs and machine learning, but the biggest feature is that you can quickly create a machine learning environment that suits your purpose. Since the system on the cloud is used, the initial investment can be reduced, and there is no need to purchase hardware, which reduces the labor and cost required to build the system. In some cases, you can use a model that has already been trained. Because of these merits, if you want to start machine learning, building using cloud services is probably the easiest way to get started.

Specific examples of machine learning research and introduction
In recent years, abundant data has become available due to the development of the Internet and the utilization of IoT . Many books dealing with machine learning expose algorithms and systems to let readers learn their know-how, helping business people who want to enter the machine learning business.

In addition, it is said that machine learning may be aimed at creating new revenue sources by improving operations and services and developing services for other companies that have the same business issues as their own.  .. In the medical field as well, the introduction of machine learning into the analysis of X-ray images is useful for early detection of illnesses, and there are also fields where results have been achieved, such as automatic document classification and automatic translation. On the research side, on the other hand, active research activities on autonomous driving of cars are famous.

Machine learning also has its limits
Machine learning is a technique for finding laws from data. Although it is a very useful tool, there are some points to be aware of when preparing learning data when introducing it.

When building a model, the preparation of learning data is a major premise. If the learning data is not prepared, in cases that are not in the past learning pattern, it cannot be recognized correctly by machine learning, and it may lead to wrong judgment.

It cannot be denied that the phenomenon of model overfitting may occur. The cause is that even if it fits the training data, due to lack of data or bias, it may create rules that cannot make predictions well, or it may apply a model that is too complicated. If you don’t have enough data, even if you introduce machine learning, you may end up with a system that is of no use.

One of the algorithms to watch out for overfitting is deep learning, which is a typical example of a complex model. Just because it is highly accurate, do not use deep learning easily, but be aware of the possibility of overfitting before using it.

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