Content
Introduction
In this page, you’ll learn…
To create a new embedded machine learning model
To use the models in Ads Configuration
Table of Contents
Overview
This guide explains how to configure Embedded ML models in Game Server so the client can download a model and its metadata, and fetch features from your app.
Create a New Embedded ML Model
Use Embedded ML models to publish a client-downloadable model plus its metadata and feature-service key. In Game Server, you’ll create a model entry with a clear name/labels, then provide URLs for the model file. Once saved, the client reads these values via the documented keys and can fetch, cache, and run the model locally. The flow below walks you through creating a model, required fields, and basic validations.
Navigate to ML: Embedded ML models and click [+New].
Fill Name : Specify a unique name.
Add Labels : Optional tags to group/find models.
Fill Settings (all mandatory unless noted):
Save.
The Feature service is a key that tells the backend which feature service to use.
Calls are made to the base URL configured in Edit Application → Feast host endpoint (Endpoint URL). The feature service key is then passed to that endpoint to select the specific service (the host can expose multiple feature services).
If the Feast host endpoint is not set, the Feature service key cannot be resolved and the feature lookup will fail. Set the host endpoint first, then provide the Feature service key.
Use the Embedded ML model in Ads Config (Ad Frequency)
This section explains how to start using an Embedded ML model and what it currently does.
What this model does (purpose)
Current purpose: Replaces the manual Frequency restriction setting in Ads Config with a model-driven ad frequency decision (computed on device from the embedded model).
Future: We may extend Embedded ML to other ad decisions, but today it affects only frequency restriction.
Where to enable it
Game Server → Ads → Ads Config → Embedded ML Config
- Select the Embedded ML model you created (or choose “None” to disable).
Behaviour & precedence
When Embedded ML Config is set, the model’s output determines ad frequency.
Setting Embedded ML Config = None reverts to manual frequency restriction.
Edit, Duplicate, Delete
Edit: Open a model: change fields: Save.
Duplicate: From the list view, use Duplicate to create a copy.
Delete: Removes the configuration (cannot be undone). Only do this if no client build depends on it.
Chunks 1
**Introduction**
In this page, you’ll learn…
- To create a new embedded machine learning model
- To use the models in Ads Configuration
**Table of Contents**
* * *
## **Overview**
This guide explains how to configure Embedded ML models in Game Server so the client can download a model and its metadata, and fetch features from your app.
* * *
## Create a New Embedded ML Model
Use Embedded ML models to publish a client-downloadable model plus its metadata and feature-service key. In Game Server, you’ll create a model entry with a clear name/labels, then provide URLs for the model file. Once saved, the client reads these values via the documented keys and can fetch, cache, and run the model locally. The flow below walks you through creating a model, required fields, and basic validations.
1. Navigate to ML: Embedded ML models and click [**+New**].
2. Fill **Name** : Specify a unique name.
3. Add **Labels** : Optional tags to group/find models.
4. Fill Settings (all mandatory unless noted):
5. **Save**.
The Feature service is a key that tells the backend which feature service to use.
- Calls are made to the base URL configured in Edit Application → Feast host endpoint (Endpoint URL). The feature service key is then passed to that endpoint to select the specific service (the host can expose multiple feature services).
- If the Feast host endpoint is not set, the Feature service key cannot be resolved and the feature lookup will fail. Set the host endpoint first, then provide the Feature service key.
* * *
## Use the **Embedded ML model** in Ads Config (Ad Frequency)
This section explains how to start using an Embedded ML model and what it currently does.
**What this model does (purpose)**
- Current purpose: Replaces the manual Frequency restriction setting in Ads Config with a model-driven ad frequency decision (computed on device from the embedded model).
- Future: We may extend Embedded ML to other ad decisions, but today it affects only frequency restriction.
**Where to enable it**
Game Server → Ads → Ads Config → _Embedded ML Config_
- Select the Embedded ML model you created (or choose “None” to disable).
**Behaviour & precedence**
- When Embedded ML Config is set, the model’s output determines ad frequency.
- Setting Embedded ML Config = None reverts to manual frequency restriction.
* * *
## Edit, Duplicate, Delete
- Edit: Open a model: change fields: Save.
- Duplicate: From the list view, use Duplicate to create a copy.
- Delete: Removes the configuration (cannot be undone). Only do this if no client build depends on it.
* * *