Workflow
STEP-1: Creating a machine learning code to train a model to distinguish between car and truck using images as a dataset.
STEP-2: Create container image that’s has Python3 and Keras or numpy installed using Dockerfile.
STEP-3: Creating a job chain of job1, job2, job3, job4 and job5 using build pipeline plugin in Jenkins.
STEP-4: Job1- Pull the Github repo automatically when some developers push repo to Github.
STEP-5: 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.
STEP-6: Job3- Training model and predicting accuracy or metrics.
STEP-7: Job4- If metrics accuracy is less than 80% , then tweak the machine learning model architecture.
STEP-8: Job5- Sending a mail that the best model is being created.
STEP-9: Job6: 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.
For More Details-> https://medium.com/@mansi.dadheech22/integrating-machine-learning-devops-a75cc896e18c