.. _introduction:

Introduction
============

The **ml-orchestrator** project is designed to streamline the management of modular components in Kubeflow Pipelines (KFP),
with a primary focus on Machine Learning (ML) workflows. For installation instructions and getting started, please refer to :doc:`usage`.

Concepts
--------

The core concept of **ml-orchestrator** is to provide a framework designed for creating, managing, and maintaining Kubeflow components.
It enables you to define component logic using dataclasses, which can easily be serialized into KFP strings.
This structured approach simplifies component management, enhances maintainability, and facilitates dependency and configuration handling.

.. list-table:: Key differences from the original KFP components definition
   :header-rows: 1

   * - Aspect
     - Original KFP
     - **ml-orchestrator**
   * - **Component Logic**
     - Defined as part of
       the function.
     - Defined within a
       dataclass.
   * - **Import & Dependencies**
     - Entire code resides

       inside the function.
     - Assumes code is

       available from the
       environment.
   * - **Testability**
     - Difficult to test;

       often requires a
       full pipeline run.
     - Easy to test;

       testable with `unittest`
       or `pytest`.
   * - **Commitment to KFP**
     - Code is tightly
       coupled with KFP.
     - Completely agnostic
       to KFP.
   * - **Flexibility**
     - Function-based logic:

       non-inheritable,

       repetitive code and
       parameters.
     - Dataclass-based logic:

       inheritable,

       reusable code and parameters.