Source: medium.com
Automating Data Science Projects with JenkinsCategory: Data, Jenkins, github, automation
Let’s paint a scenario, you're working on a Data Science project and at first, you had a model accuracy of 80%, you deploy that application to production serving it as an API using Flask. Now you plan to deploy this model and you have to go through the trouble of building, testing and deploying the model to production again which is a lot of work.
Jenkins has a lot of amazing features and some are beyond the scope of this article, to get the hang of Jenkins you can check the documentation.
Startup a Jenkins server and install Git, Docker, Pipeline and build plugins and also install Git and Docker in your instance also.
To do this, you need to add the Jenkins webhook to the GitHub repository for Github to communicate to Jenkins if there's a change in the code.
Jenkins has a lot of amazing features and some are beyond the scope of this article, to get the hang of Jenkins you can check the documentation.
Startup a Jenkins server and install Git, Docker, Pipeline and build plugins and also install Git and Docker in your instance also.
To do this, you need to add the Jenkins webhook to the GitHub repository for Github to communicate to Jenkins if there's a change in the code.
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