I am researching gaze estimation using deep learning, but I feel that it takes a lot of time and effort to build and implement the environment when actually performing deep learning.
Therefore, when I first learned about cloud services such as AWS, I was very surprised that development could be done so easily, and started to take an interest.
In recent years, the development of AI technology such as deep learning has been actively carried out.
However , it is also true that AI development requires a lot of time and effort to prepare the development environment, collect data, and learn .
In recent years, we have been providing a one-stop service from development environment to maintenance and operation.
- Amazon Web Services (AWS)
- Microsoft Azure
- Google Cloud Platform (GCP)
The use of cloud services such as
Therefore, Skill Up AI will hold a course where you can systematically learn cloud development using AWS, which boasts the largest share among cloud services .
In this course, what is a cloud service in the first place? From the story, you can learn through hands-on how to actually implement AI services using the cloud, and you can experience the actual flow of AI development in your company in two days.
Below, we will introduce the contents of the course that you can experience on the day!
- Why use cloud services for development?
- Development using learned AI services
- Building a big data processing platform
- Implement and operate machine learning models
- in conclusion
In the first place, why is it cloud instead of on-premises (a form in which all necessary IT resources such as data centers and computers are owned, constructed and operated by the company)?
In AI development, it is necessary to understand the characteristics of the cloud and on-premises, and then decide which one to adopt and develop.
Comparing the characteristics of the cloud and on-premises, if you already have a data center in your company and have accumulated know-how on operations, etc., on-premises can be said to be more flexible and advantageous . .
However, if you want to introduce a new development environment, you can see that the cloud has the advantage of being easier to introduce . In fact, the use of the cloud is on the rise, and the introduction of the cloud has become an important option for advancing AI development.
AWS not only provides a development environment, but you can also use trained AI services.
Although the pre-trained AI service has the disadvantage of being less customizable than self-development, it has the advantage of shortening the development period because it saves the effort of learning .
In the learned AI service,
- object recognition
- text translation
- audio transcription
It provides many basic functions such as
Various functions can be realized by combining these well, and by using them well, it will lead to shortening the development period.
Collecting data is important for AI development. AWS provides tools that automatically perform a series of operations from collecting data, storing it in storage, and visualizing the collected data. In the course, you will learn the flow of data processing through hands-on .
In addition, it is necessary to build a database for the collected data and make it easy to handle. In the course, we will also cover Hadoop, a software group for handling big data. Again, through hands-on learning, you will learn how to build a database from the data you collect.
Once you understand data engineering (preparing the environment for smooth data analysis), you will learn how to use data to develop machine learning models .
In this course, we will learn the flow from learning, deployment, and inference in hands-on using SageMaker , a machine learning development environment provided by AWS, with anomaly detection algorithms* as the subject .
*SageMaker sometimes refers to machine learning models as algorithms
Hands-on, you’ll learn how to:
- Development using built-in algorithms provided by SageMaker
- Development using Scikit-learn framework-based algorithms
- Development using proprietary algorithms
SageMaker has many built-in algorithms and can be easily deployed to the cloud, so it has the advantage of very high development efficiency . In the course, we will do a lot of hands-on using SageMaker.
It is also possible to implement machine learning models using machine learning frameworks such as Scikit-learn instead of the built-in algorithms provided by SageMaker. By actually working on these hands-on, you can experience how flexible development is done according to the situation.
Ultimately, you will be asked to think about a specific problem and design a solution. By actually designing solutions, we aim to acquire the practical ability to flexibly address various real-world problems.
What do you think? This is a must-see course for those who want to actually develop AI services using cloud services.
It is an interactive course where you can actually move your hands and experience active learning in addition to classroom lectures, so if you are interested, please take a look at the course page.