Source: medium.com
Integrating ML with DevOpsCategory: Docker, Jenkins, github, automation, machine-learning
The purpose of this article is to demonstrate integration of Machine Learning with DevOps. We will be performing following tools and technologies to perform the practical: Machine Learning : Libraries Required : DevOps : Tools Required : We will create Tasks in Jenkins for automating the whole process : Job1 : Pull the Github repo automatically when some developers push ML Model to Github.
Job3 :Jenkins should automatically start the Docker Container with respective libraries required for running the code (i.e For Simple Regression Model we just need numpy, pandas and sklearn, but for CNN we also need Tensorflow and Keras).
If expected accuracy of ML model is not achieved, Developer will Fine-Tune the model and again will push the new model to github repository.
Now, as we desired our application is now launched on docker container, and we can checkout the website.
Job3 :Jenkins should automatically start the Docker Container with respective libraries required for running the code (i.e For Simple Regression Model we just need numpy, pandas and sklearn, but for CNN we also need Tensorflow and Keras).
If expected accuracy of ML model is not achieved, Developer will Fine-Tune the model and again will push the new model to github repository.
Now, as we desired our application is now launched on docker container, and we can checkout the website.
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