# Workflows

## Overview
The Operational Machine Learning Lifecycle has multiple stages that are interwoven to create robust, reliable, and generalizable models.

The workflows described in this document give an overview to the role-specific stages that you might be tasked with. \
It is important to note that these workflows are flexible, and many times multiple hats are needed in a professional setting.

However, at DataEval, we have created a few entry-level guides for common roles found in Data Analytics and Machine Learning to develop the crucial knowledge one must know to become an expert practitioner.

## Roles

:::{toctree}
:maxdepth: 1

Testing & Evaluation Engineer <TE_engineer_workflow.md>
Artificial Intelligence Engineer <AI_engineer_workflow.md>
:::

## See Also

For more information, visit our in-depth explanation on the entire operational machine learning lifecycle.\
Here you will learn about each stage, how they interact, and how they differ from competition-based machine learning life cycles