Content
Introduction
In this page, you’ll learn…
To create a game-specific 3rd-party machine learning model
To use the models in game settings
Table of Contents
Overview
This guide explains how to configure game-specific 3rd-party machine learning models in Game Server so the client can download a model and its metadata, and fetch features from your app.
Creating a New Game-Specific 3rd-Party Machine Learning Model
This section shows how to create and manage a Game-Specific 3rd-Party ML Model in Game Server, configure its inference endpoint, and set request parameters the client will send when calling your model.
This feature is not available in all games. Refer to this section only if your application supports Game-Specific 3rd-Party ML models and you have an active inference endpoint.
Perform the following steps to create a new classic ML model:
Navigate to ML: ML models and click [+New].
Fill Name: Specify a unique name.
Type: Choose between the options: Classic or Journey. Available settings depend on the Type. If you’re using an endpoint for classic, select Classic and configure its settings.
Add Labels: Optional tags to group/find models.
Add the Endpoint (required): The HTTPS URL the server will call to obtain predictions.
Specify the Model request parameters: These flags and values control what the model should compute/return. They are sent along with the request. Enable only what your model supports:
| Setting | Classic | Journey | Description |
| --- | --- | --- | --- |
| Number to configs to return | ✓ | ✓ | Integer count of configuration candidates to return. |
| ML for All Shapes Placeable enabled | – | ✓ | Only return configurations where all shapes are placeable (Journey constraint). |
| ML for Perfect Fit enabled | ✓ | ✓ | Ask for a “perfect-fit” option (tightest match to player/context). |
| ML for Complementary fit enabled | ✓ | ✓ | Request an option that complements recent player behaviour. |
| ML for Terminator enabled | ✓ | ✓ | Include a fail-safe/“terminator” option to fall back to. |
| ML for Seeds | ✓ | – | Return deterministic seed values (Classic only). |
| ML for Figure sets | ✓ | – | Return figure-set–aware configurations. |
- Click Save.
Select an ML Model in Game Settings
This section tells the client which model to use and how many recent events it must send so the model can produce a prediction.
Where to set it
Game Server → Game Settings:
ML Model for Classic
ML Model for Journey
Fields
| Block | Field | What it does | Notes |
| --- | --- | --- | --- |
| Classic / Journey | ML configuration ID | Pick the ML Model you created earlier. Selecting a model sends a boolean to the client indicating that ML is enabled and which configuration to use. | Choose None to disable ML for this game. |
| Classic | Number of events for ML model | How many of the latest Game End events the client should send to Game Server for the model to analyse. | Must be a positive integer. |
| Journey | Max number of game end events for ML model | Same as above, but for the Journey flow. | Must be a positive integer. |
Validation tips
Leaving ML configuration ID as None means ML is off for the client.
Event count 0 disables sending events.
End-to-end flow (what happens after you save)
- Config to client:
- Game Server sends down:
- Client collects events:
- After the defined client trigger (e.g., game win or game start), the client sends the latest N Game End events to Game Server.
- Server → ML endpoint
- Game Server forwards those events to the selected ML Model’s endpoint together with the request parameters you configured in the ML Model (e.g., number of configs to return, perfect/complementary fit, terminator, seeds/figure sets, etc.).
- ML inference
- The ML service returns results based on the chosen ML configuration (Classic or Journey).
- Results to client
- Game Server returns the model’s result to the client, which then applies the recommended configuration.
Chunks 1
**Introduction**
In this page, you’ll learn…
- To create a game-specific 3rd-party machine learning model
- To use the models in game settings
**Table of Contents**
* * *
## **Overview**
This guide explains how to configure game-specific 3rd-party machine learning models in Game Server so the client can download a model and its metadata, and fetch features from your app.
* * *
## Creating a New Game-Specific 3rd-Party Machine Learning Model
This section shows how to create and manage a Game-Specific 3rd-Party ML Model in Game Server, configure its inference endpoint, and set request parameters the client will send when calling your model.
This feature is **not available in all games**. Refer to this section **only if your application supports Game-Specific 3rd-Party ML models** and you have an active inference endpoint.
Perform the following steps to create a new classic ML model:
1. Navigate to ML: ML models and click [**+New**].
2. Fill Name: Specify a unique name.
3. Type: Choose between the options: Classic or Journey. Available settings depend on the Type. If you’re using an endpoint for classic, select Classic and configure its settings.
4. Add Labels: Optional tags to group/find models.
5. Add the Endpoint _(required)_: The HTTPS URL the server will call to obtain predictions.
6. Specify the Model request parameters: These flags and values control what the model should compute/return. They are sent along with the request. Enable only what your model supports:
| **Setting** | **Classic** | **Journey** | **Description** |
| --- | --- | --- | --- |
| **Number to configs to return** | ✓ | ✓ | Integer count of configuration candidates to return. |
| **ML for All Shapes Placeable enabled** | – | ✓ | Only return configurations where _all shapes are placeable_ (Journey constraint). |
| **ML for Perfect Fit enabled** | ✓ | ✓ | Ask for a “perfect-fit” option (tightest match to player/context). |
| **ML for Complementary fit enabled** | ✓ | ✓ | Request an option that complements recent player behaviour. |
| **ML for Terminator enabled** | ✓ | ✓ | Include a fail-safe/“terminator” option to fall back to. |
| **ML for Seeds** | ✓ | – | Return deterministic seed values (Classic only). |
| **ML for Figure sets** | ✓ | – | Return figure-set–aware configurations. |
1. Click Save.
* * *
## Select an ML Model in **Game Settings**
This section tells the client which model to use and how many recent events it must send so the model can produce a prediction.
**Where to set it**
Game Server → Game Settings:
- ML Model for Classic
- ML Model for Journey
**Fields**
| **Block** | **Field** | **What it does** | **Notes** |
| --- | --- | --- | --- |
| Classic / Journey | **ML configuration ID** | Pick the ML Model you created earlier. Selecting a model sends a boolean to the client indicating that ML is enabled and which configuration to use. | Choose None to disable ML for this game. |
| Classic | **Number of events for ML model** | How many of the latest Game End events the client should send to Game Server for the model to analyse. | Must be a positive integer. |
| Journey | **Max number of game end events for ML model** | Same as above, but for the Journey flow. | Must be a positive integer. |
> **Validation tips**
>
> - Leaving ML configuration ID as None means ML is off for the client.
>
> - Event count 0 disables sending events.
* * *
## End-to-end flow (what happens after you save)
1. Config to client:
- Game Server sends down:
1. Client collects events:
- After the defined client trigger (e.g., _game win_ or _game start_), the client sends the latest N Game End events to Game Server.
1. Server → ML endpoint
- Game Server forwards those events to the selected ML Model’s endpoint together with the request parameters you configured in the ML Model (e.g., number of configs to return, perfect/complementary fit, terminator, seeds/figure sets, etc.).
1. ML inference
- The ML service returns results based on the chosen ML configuration (Classic or Journey).
1. Results to client
- Game Server returns the model’s result to the client, which then applies the recommended configuration.
* * *