In this task, we apply knowledge of machine learning, git, Github, Jenkins, and Docker.
step1:-Create a program that applies machine learning and predicts accuracy.
step2:- Create a repository and upload the code Github.
step3:-Create a container use of docker concept.
step4:- Create a Jenkins job copying the code on Github.
step5:- Create a Jenkins job to run the code.
step6:- Create a python code to send the notification if the accuracy is less than the required accuracy.
Step1:- Create a Program of Machine learning
In this Machine Learning Program, create a model of the KNN algorithm and find the accuracy of the model.
step2: Use Git Command
git commit -m “<commnet>”
use of hooks to push the file automatically on Github.
Step3:- Create a Docker image
In this step, we create a docker image to install all the libraries used to run the machine learning program.
This command used to open the text file, the filename is Dockerfile because of all the docker images mandatory the put name Dockerfile.
step4:-Create a Jenkins job copying the code on Github.
Use of Jenkin's concept to create the job to run automatically.
Create a job copy of the code on Github and set up the webhooks when any updates in the code download automatically on GitHub.
Create a job running the machine learning container and run the python code and finding the accuracy of the code.
In this job first, condition checks all the docker files and check the python_space container if container finds then container start if not found the container create and run the container.
In this job, find the accuracy and if accuracy is lower than required accuracy then print the message and run the next job and send the notification for failure.
In this job send the notification on the developer of failure and success of the code or accuracy.
Thank you, Vimal Daga sir has given high-level concepts to solve many use cases.