Integration of Machine Learning with DevOps (MLOps)

Description and Steps:-

1. Create container image that’s has Python3 and Keras or numpy installed using dockerfile

Goal: When we launch this image, it should automatically starts train the model in the container.

2. Create a job chain of job1, job2, job3, job4 and job5 using build pipeline plugin in Jenkins

Job1 : Pull the Github repo automatically when some developers push repo to Github.

Job2 : Job2 : By looking at the code or program file, Jenkins should automatically start the respective machine learning software installed interpreter install image container to deploy code and start training( eg. If code uses CNN, then Jenkins should start the container that has already installed all the softwares required for the cnn processing).

Job3 : Train your model and predict accuracy or metrics.

Job4 : if metrics accuracy is less than 90% , then tweak the machine learning model architecture.

Send the mail to the developer.

Job5: Retrain the model if accuracy is less than 90% and notify that the best model is being created

Job6 : Create One extra job for monitor : If container where app is running. fails due to any reason then this job should automatically start the container again from where the last trained model left

Build Pipeline View:

Final Output of Model: