A machine learning (ML) pipeline is a series of steps that automate the process of creating an ML model.
It includes raw data input, features, outputs, the ML model, model parameters, and prediction outputs. The pipeline's purpose is to streamline data analytics and ML processes.
For data science teams, the production pipeline should be the central product. It encapsulates all the learned best practices of producing a machine learning model for the organization’s use-case and allows the team to execute at scale. Whether you are maintaining multiple models in production or supporting a single model that needs to be updated frequently, an end-to-end machine learning pipeline is a must.
