Machine Learning Over Docker

Hello connections,

Hope you are safe and doing well :)

In this article, I am going to do the task which was assigned to me in the Summer training internship. In this I will going to integrate Machine learning with docker and make a small application which will able to predict salary by taking work experience.

Task Overview

👉 Pull the Docker container image of CentOS image from DockerHub and create a new container

👉 Install the Python software on the top of docker container

👉 In Container you need to copy/create machine learning model which you have created in jupyter notebook

👉 Create a blog/article/video step by step you have done in completing this task.

👉 Submit the link of blog/article or video

What is Machine Learning?

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

What is Docker?

Docker is a software platform that allows you to build, test, and deploy applications quickly. Docker packages software into standardized units called containers that have everything the software needs to run including libraries, system tools, code, and runtime.

Steps to follow :

First, You have a Linux installed in your system preferably RedHat Linux (REHL 8).

Next, you should configured YUM on it.

Now, You should have docker installed on linux.

Now, I will procced

Step 1 : Start the docker services by the following command :

$ systemctl start docker

Step 2 : Pull the centos image by :

$ docker pull centos:8

to check centos is pulled use :

$ docker images

Step 3 : Launch a container named mlops by using :

docker run -it — name mlops centos:8

Step 4 : Install pyhton on it

Install pyhton
Install python libraries

Step 5: Make a folder named task01

Step 6: install git to clone your repository

Install git inside container

Step 7: Clone your repository and move inside the directory.

Cloning the GitHub Repository

Finally run the python file and see the result.

For the GitHub link you can visit :

That’s all for this blog, hope you liked it.

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