Obstacles and solutions of MLOps

MLops receive a lot of attention for its advantages in automated management. However, there is also difficulty in introducing it, so let's take a look at the solution of DS2.ai.

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Obstacles and solutions of MLOps

​ ​ ● Intro
​ Today’s Blog posting will cover an overall description of MLops, a topic that has recently attracted attention in ML projects. And how DS2.ai utilizes MLops.
​ ​ ● What is MLOps?
​ The basis of the Machine Learning (ML) project, which has established itself in data utilization for many years, is to identify and leverage the various patterns hidden in the data collected.

However, one 'ML project' is not just about creating one model. You need to think about how to get and process data, how to create and evaluate models, and how to connect and maintain them as services if you’ve created a model you like.

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(ML life cycle. Many procedures are needed before and after 'Making model.)

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(Google-expressed MLOps format also shows that ML code is only one tiny element.)

In the end, you can approach MLOps as the whole methodology that leads to actual production.

From defining the problem to monitoring when it’s released later, it takes a series of tasks to go back to the beginning and go through relearning and redistribution.

​ ● MLOps, how important is it?
​I briefly explained what concept MLOps is. I would like to go a step further and talk more about the utility and necessity of MLops.

When the company develops, the project is not carried out simply by developers. The entire project build process from a business perspective must also involve the operational steps of subsequent deployment and testing. MLOps is a fusion of experimental (ML) and development (Develop) operations (Operate) from this perspective.

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(Chart of MLops. A single loop enables stable ML project progress.)

​ In other words, you can understand that MLOps is an essential concept for stable and fast progress for a successful ML project.

Strengths of DS2.ai MLOps
Typically, the introduction of MLOps requires setting up a cloud server for businesses to use, and then going through a complex way of implementing backend servers and installing MLOps. It’s a procedure that requires high cost and manpowers. DS2.ai selected API as a means for the artificial intelligence model and supply it in an easily applicable form.
​ First of all, we created a backend automation deployment solution called Skyhub AI. If you use this solution, you can supply AI in an applicable form just by uploading the AI model. Which requires at least a few days of development with traditional way, but using Skyhub AI, you can rent and set servers for less than 30 minutes. This technology is a meaningful technology that has completed a patent application by our company.

Since then, MLOps has been able to automatically upgrade to better AI, based on accumulated data from customers using separate databases in the process of monitoring their businesses. And it will address the problem continuously.​

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(Main Control Panel and Server Lease Page of Skyhub AI Solution: Operation Screen)

In addition, for those who are from development background, we also introduced a plan to extract models with APIs and utilize artificial intelligence in the form of programming languages.

An API is an interface that allows you to control the functionality provided by an operating system or programming language for use in an application, that is, a medium that allows programs to interact with each other.​ ​ As a result, users can easily introduce and use a management system by skipping complicated processes by using our MLOps system.
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(Users can easily use S/W through the API)

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(API files extracted in various programming languages)


In this post, MLOps solution and how DS2.ai introduced it were discussed.
We’ll come back with the overview of our solution sales/distribution practices.

Check out Skyhub AI, solutions ranging from setting up various environments to monitoring to automatic upgrades for business operations!

References : Microsoft Korea - ML Life Cycle Hidden technical debt in ML systems