This article describes how to add and manage an Implementation (Model), which is the technical approach used to address a business use case.

Table of Contents

Introduction

ModelOp Center is flexible on how Model Implementations are registered so that data scientists can leverage their preferred environments for model creation.

• ModelOp Center is agnostic to the toolkit used to create the model.

• You can register the model using ModelOp UI, API, CLI, or the ModelOp Center Jupyter Plugin

• Each organization has unique requirements on the information, metadata, assets, and documentation that is required, and thus ModelOp Center is flexible in what input the user needs to provide. Generally, most organizations require the following information to be added to the model record:

  • Model Information: Model Name, Description, Organization, Methodology, Model Risk

  • Model Source: git repository for all source code assets

  • Documentation: the documentation for the model

  • Schema: the description of the data that is used for the model

To get started

Get started by adding an Implementation to ModelOp Center. See the Add an Implementation article

Next Article: Add an Implementation >