Background: The most technical challenge for any new machine developer is to manually run the code, check the error and then increase the accuracy. By using Jenkins it reduces the manual part which is the most exciting part. It is not only running the code but taking the code from GitHub itself and setting up the environment using the docker container. Then it runs the Ml code, and if the accuracy of the model is less than 80% it will alert through an email -- this reduces the developer workload. This alert system automatically provides feedback to developers so that they can easily detect where they are lagging and do the correction on time. This will not only reduce the error but also increase the accuracy of the model.
Goals: Increasing the accuracy of a Machine Learning model and alert when it fails to reach the desired accuracy.
Solution & Results: First, I created a docker file in which I wrote all the dependencies required to run my ML model. Next, I wrote the CNN -- convolutional neural network -- code using Python programming language. I then created one GitHub repo where I stored all my codes. Then I installed Jenkins on Redhat and exposed the port to use Jenkins in my local browser. I installed all the plugins and created five jobs:
Jenkins is the best technology in my opinion. We used plugins like GitHub , SMTP, and many more which makes my work easy and provides me with such an automated environment that I can honestly say, "Jenkins, YOU ROCK!
The key capabilities we relied on were:
For this project, the results were: