An ML pipeline is characteristically used to automate ML workflows, essentially enabling sets of data to be transformed and correlated in a model that can then be tested and evaluated to achieve or estimate an outcome.

Such a workflow consists of four basic areas:

• Data preparation
• Training set generation
• Algorithm training/evaluation/selection 
• Deployment/monitoring