In this article, we have automated system such that if "developer1" do some upgrade in the main developer( in the master branch), that work is first tested in the testing environment and it is checked by the quality management team and if work passes all quality norms, a token will be triggered by Quality management team, that will trigger JOB3 so that it can merge two branches and update the main branch to the main environment and destroy the testing environment.

This article is all about integrating Jenkins,Terraform and AWS.Nowadays we are using multi-cloud architecture for cloud computing.Managing this is a big headache.Here we can use terraform which make things easier.From this we can manage almost all cloud computing services.Today in this article we are going to make a big automated infrastructure from terraform. I have tried to do most things automated.

This article is based on how to integrate prometheus and grafana using the kubernetes.We will be launching prometheus and grafana on the top of the orchestration tool kubernetes.We will be covering the general problem we face while launching prometheus and grafana. In general, we configure prometheus.yml file to get the data of the target node. But suppose if our pods or our deployment gets deleted then we have to again configure the prometheus.yml file and doing this thing, again and again is not a good practice. Today we will see how to launch Prometheus so that we don't have to configure promtheus.yml file again and again even if the pods or deployment gets deleted.

This article helps you to automate Web deployment with Kubernetes. Generally, it is not an easy task to fetch the code of a developer from the Jenkins and make Operating System according to his/her need and upload it on the docker hub and from there we can pull the image to deploy it on Kubernetes and Kubernetes will launch the pod. Isn't it a very long and tired process to do it again and again ??

Training the model in Deep Learning is not a big deal, but changing the hyper-parameters for getting a better accuracy matters a lot which is a tedious process.

This Article Covers a great Problem Statement that how we can automate the Deep Learning so that it can achieve a High Accuracy, Less Training Time and Less Resources automatically without any human intervention. This reduces the tedious work that has to be done by the humans.

In this article we will see how to make our own Jenkins Docker image so that on one click, Jenkins will be loaded. We will also see how to automatically open language suitable interpeter.