Deploying Machine Learning Model In Docker

In this article, we are going to deploy a machine learning model on docker.

What is Docker

Docker is hotter than hot because it makes it possible to get far more apps running on the same old servers and it also makes it very easy to package and ship programs. Here’s what you need to know about it.

Docker is a software platform for building applications based on containers — small and lightweight execution environments that make shared use of the operating system kernel but otherwise run in isolation from one another.

Using the salary dataset, we are going to make one simple linear regression model and deploy it on docker.

Step 1: Configuring Yum Repository For Docker

First, we have created a docker12.repo and in that, we have provided the baseurl , ID, gpgcheck and we can check that by using the cat command.

Step 2: Installing Docker Community Edition

yum install docker-ce — nobest -y

By using this command are installing docker community version.

Step 3: Enabling the Docker Service in RHEL 8

systemctl restart docker

systemctl enable docker

It’s useful to restart our docker service, then use we have used enable command to enable the docker service and after rebooting the docker service will be available to us.

systemctl status docker

Step 4: Enable Firewall for Docker Engine

Step 5: Pulling the Latest Centos Docker Image

Step 6->> Create a Docker Container

Step 7: Installing Python and Git in centos (i.e. in nested os)

Step 9: Creating ml regression model

Thanks for reading ..