Operationalizing Models: ModelOp Runtime as REST

This article describes how to prepare the ModelOp Runtime and the MLC Process to deploy a model.

Table of Contents



Operationalize a Model in a ModelOp Runtime - ONLINE Deployment

The following are the steps to deploy a model as REST via ModelOp Center:


Prior to any model deployment, the following steps should be conducted by the Enterprise AI Architect and ModelOps team.

  1. Runtime Environments/Stages. Define the Runtime Environments/Stages required for operationalizing models for a given team and/or class of models. For example, if this team requires a model to be deployed and tested in Development, then SIT, then UAT, before being promoted to Production.

  2. Operationalization MLC. The Enterprise AI Architect and/or ModelOps Engineer will create the model life cycles (MLC’s) required for operationalization models for that team and/or class of models. This operationalization MLC will take into account the technical and governance requirements as a model is operationalized, which may include running tests, obtaining approvals, promoting the model to the next higher Runtime Environment (e.g. from Dev to QA).

Note, each Business Unit / Team may have different requirements and therefore these steps may be updated or repeated as the overall program progresses.



Prepare Runtimes.

  1. Identify the target Runtimes across the requisite Environments. For each target Runtime, complete the following:

    1. Add Runtime Endpoints. If any runtimes will be used to provide an online model, add the required Endpoints.

      1. To add an endpoint, follow these instructions: Adding Endpoints to a ModelOp Runtime

      2. For runtimes that will be used for Batch runs of the model, do not put endpoints on the model.

    2. Add “Environment/Stage Tags”. Based on the environments/stages required (see pre-requisites), add the necessary “environment/stage tag” to the runtime.

      1. Example: add a “DEV” tag to the Runtime in their development environment, an “SIT” tag to the Runtime in their SIT environment, a “UAT” tag to the Runtime in their UAT environment, and ultimately a “PROD” tag to the Runtime in their Prod environment

    3. Add “Model Service Tags”. The Model “Service” tag will be used to identify that this specific runtime is designed to be a target runtime for that particular model. Add the appropriate “Model Service Tag” to the runtime.

      1. Example: add a “cc-fraud” Model Service Tag to the runtime for a 3rd party credit card model to the “Dev”, “SIT”, “UAT”, and “Prod” runtimes.


Create a Snapshot of the model.


Related Articles

Next Article: Operationalize a Model - Batch >