Machine Learning with DevOps (MLops)
- Creating container image with Python3 and Keras or numpy installed using dockerfile.
When we launch this image , it should start training the model in the container.
2. Creating 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 : 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
Job3 : Train your model and predict accuracy or metrics.
Job4 : if metrics accuracy is less than 90% , then tweak the machine learning model architecture. Then send mail to the developer.
Job5: Retrain the model or 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.
After creating Job 6 Our build Pipeline view look like-
This was the integration of the Machine Learning with the DevOps ,where integrating different tools for different purpose was done accordingly.
