Kubeflow Pipeline For Production Systems

These steps are often executed manually by running scripts, notebooks, and applying human judgment to validate the model as successfully trained. After model training for production, the process of training an ML model is more likely to be automated with an ML model training pipeline. This way, the model training can be reviewed, reproduced, tuned, and debugged at any point. When triggered, the components in the above pipeline will run in sequential order, and only if they are all successful, a micro-service that serves the trained model will be created or updated. Most of the components in the above pipeline can be reused and have open-source implementations.