Versions Compared

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

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

Table of Contents

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:

Pre-Requisites:

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.

Steps:

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.

  1. From the Production Model Inventory, open the specific model that you would like to snapshot. Select the “Create Snapshot” button from the top right.

Image Removed

2. Optionally, add a Name and Description for the snapshot. Most important, add the same “Model Service Tag” that was added to the requisite Runtimes (see above). This will ensure that the MLC will know the correct Runtimes to which the model should be deployed throughout the life cycle.

  1. Example: add a “cc-fraud” Model Service Tag to the snapshot.

Image Removed

3. Optionally suggest the Runtime Type OR the specific Runtimes that will be used by the model during its life cycle, or simply allow the MLC to find the best runtime for the model.

Image Removed

4. Optionally add any monitors (called “associated models”)

Image Removed

5. Review the Snapshot details. When ready, select “Create Snapshot”.

Image Removed

6. The snapshot details page will be opened with all of the details provided during the create snapshot process.

Image Removed

7. Additionally, the requisite Model Life Cycle will automatically begin orchestrating all of the steps necessary to operationalize the model. This includes deployment of the model as REST into the designated ModelOp runtimes or the best matching available ModelOp runtime. The details of the model life cycle can be seen in the “Model Life Cycle” section of the snapshot page.

Image Removed

8. Note, model life cycle updates and any errors in the model life cycle execution will be populated in the “Notifications” pane of the home page. As well, depending on the environment configuration, Jira and/or ServiceNow tickets may also be raised in the event of an issue. Granular logs can be found in the “MLC Manager” docker logs, which typically are configured to write to ELK or a similar enterprise log aggregation and visualization capability.

Image Removed

Related Articles

Next Article: Model Batch Jobs and Tests

Include Page
Create a Model Snapshot
Create a Model Snapshot

Related Articles

Next Article: Operationalize a Model - Batch >