Using Container For Machine Learning Application

In the last article, we discussed deployment and production environment such that it consisted of two primary programs, the application itself and the model by communicating with each other using an interface we know as the endpoint. Today, we’ll learn about the Containers and how Machine Learning Applications can benefit from them. We’ll learn briefly about Model, Application, and then dive into Containers and specifically Docker and explore its structure and its advantages.

Model

To understand the Model, let us take an instance of a simple Python model which is to be created, trained, and validated in the modeling component part of the machine learning workflow discussed in Machine Learning Workflow And Its Methods Of Deployment.

Application

Application is basically a software or web application that provides users who use the application to benefits from the model by retrieving predictions.

Containers

Containers can be defined as a unit of software that is executable where the application codes are packaged with the needed dependencies and libraries such that it can be run where ever needed from cloud to on-premises services.

Docker

Docker is a platform that supports to development, ship, and run of applications by delivering software in packages that are known as containers.

Understanding Containers

To understand anything using an analogy to pre-existing knowledge is fundamental. Using this approach let’s understand containers here in comparison to shipping containers. The shipping containers contain multitudes of variations of products ranging from cars to food to furniture and so much more. The very structure of these shipping containers makes it possible to load, unload, transport all of these products across the globe within a container no matter how diverse these products are. Similarly, the Docker containers in our context also have the capability to contain multitudes of different types of software and the structure of the Docker container provides the feasibility with a set of common tools to create, save, use and delete the containers. Regardless of the types of software that these containers contain, the common toolset can work for any container.

Understanding the Structure of the Container

  • Security risks are mitigated as the application is isolated and thus, the system becomes more secure.
  • Across all the applications which get deployed on the container, these applications will have swift access to be created, replicated, deleted with easier maintenance provision.
  • The containers themselves can be replicated, saved, and shared across in a more simplistic and secure manner.

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