Core Concepts

MLeap uses several core building blocks to deploy your pipelines in an easy-to-use manner.

Concept Description
Data Frames Used to store data that is to be transformed, similar to a SQL table
Transformers Take data from a data frame, apply some operation to it, and output new fields into the data frame
Pipelines Use pipelines to execute a series of transformers against a data frame
Feature Unions (Scikit Only) Use feature unions to execute Pipelines of transformers in parallel and join results at the end
MLeap Bundles Used to store ML pipelines in a common JSON/Protobuf serialization format
MLeap Runtime Used to execute an ML pipeline in the JVM using lightweight data structures

This section is meant as an introduction to people who are unfamiliar with the basics of machine learning pipelines and working with data frames. Although, the sections on MLeap Bundles and the MLeap Runtime should be useful to everyone.

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