> ## Documentation Index
> Fetch the complete documentation index at: https://docs.neuronsearchlab.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Models

> Manage the trained models that power your recommendations and control which version is live in production.

A trained version is the output of a training run. A registered model is the long-lived operator-facing track you use to organise versions, decide what should stay active, and manage ongoing rollout decisions.

The important mental model is:

* **Events** define what the system should learn from
* **Templates** package those signals into a repeatable training recipe
* **Training runs** create new trained versions
* **Models** are optional long-lived tracks you use to organise versions over time
* **Serving targets** decide which approved version is live for a given tenant or surface

***

## How To Think About Training

Users should think about training and retraining as a versioning workflow, not as editing one monolithic model in place.

<Steps>
  <Step title="Create or update signals">
    Create or update a signal template on the **Events** page.
  </Step>

  <Step title="Start training">
    Start a training run from that template.
  </Step>

  <Step title="Create a trained version">
    Let the platform create a **new trained version**.
  </Step>

  <Step title="Review the result">
    Review the result on the **Models** page.
  </Step>

  <Step title="Add a label">
    Optionally add a human-friendly label and description.
  </Step>

  <Step title="Approve the version">
    Approve the trained version when it is ready.
  </Step>

  <Step title="Promote to serving">
    Promote it to a serving target when you want it live.
  </Step>

  <Step title="Repeat when retraining">
    Repeat the same flow whenever you retrain.
  </Step>
</Steps>

That means retraining does **not** overwrite the previous result. It creates another version you can compare, approve, reject, or promote.

## Training And Retraining Flow

```mermaid theme={null}
flowchart TD
    A["Define signals on Events page"] --> B["Save template"]
    B --> C["Start training run"]
    C --> D["Platform creates new trained version"]
    D --> E["Review version on Models page"]
    E --> F["Optional: add label and description"]
    F --> G{"Ready for use?"}
    G -->|No| H["Leave unapproved or retrain again"]
    G -->|Yes| I["Approve version"]
    I --> J["Promote to serving target"]
    J --> K["Version becomes active"]
    K --> L["Later retraining creates another new version"]
    L --> E
```

## System Name vs Human Label

Every trained version has two layers of identity:

* A **system name** generated from the template name and the training run identifier
* An optional **label** and **description** added by your team

Example:

* System name: `Engagement booster v2 · train-42-1712912345`
* Label: `Homepage recommender`
* Description: `Tighter freshness weighting for spring campaign`

The system name preserves provenance. The label and description make the version easier for operators to recognise later.

## Registered Models vs Trained Versions

Use these two concepts differently:

* **Trained versions** are the raw outputs of training runs
* **Registered models** are long-lived tracks you create when you want an explicit operator-owned model record for rollout, version management, or ongoing retraining policy

A common pattern is:

* Train several versions from one template
* Review and compare them
* Promote one version
* Later create a registered model if you want a named track for that recommendation strategy

## What The Models Page Shows

The Models page brings together:

* Successful trained versions from recent runs
* Approval state
* Serving targets
* Which version is active
* Registered models, if you use them as long-lived tracks

This lets you operate even if you are a brand-new account with no pre-existing model records.

***

## Approve A Trained Version

After a training run completes successfully, the new trained version has a status of **Pending**. You must approve it before it can be promoted.

<Steps>
  <Step title="Open training or models">
    Open [Console > Training Jobs](https://console.neuronsearchlab.com/training-jobs) or [Console > Models](https://console.neuronsearchlab.com/models).
  </Step>

  <Step title="Approve the completed version">
    Find the completed version and click **Approve**.
  </Step>

  <Step title="Confirm approved status">
    The version status updates to **Approved**.
  </Step>
</Steps>

Approving a version does not automatically change what is served. It makes that version eligible for promotion.

***

## Promote A Version To Production

Once approved, you can promote a version to replace what is currently live on a serving target.

<Steps>
  <Step title="Find the approved version">
    Find the approved trained version.
  </Step>

  <Step title="Start promotion">
    Click **Promote to production**.
  </Step>

  <Step title="Choose a serving target">
    Choose the serving target you want to update.
  </Step>

  <Step title="Watch target status">
    The console will show the target status updating.
  </Step>

  <Step title="Confirm production traffic">
    Once the target returns to a healthy state, the promoted version is live and subsequent recommendation requests use it.
  </Step>
</Steps>

You can promote a previous version at any time if you need to roll back.

***

## Active Version

The Models page shows which version is currently active on each serving target. That gives you a clear answer to:

* Which version is live?
* Where is it live?
* When was it last promoted?

If no version is active yet, the target remains unassigned until you promote one.

***

## Training run metrics

Each training run records a set of metrics at completion. Open a run from the Training Jobs page to view:

* Training job status and duration
* Final metrics (loss, accuracy, or custom metrics your training job reports)
* The run manifest, which records the configuration used

Use these metrics to compare versions and decide whether a new one is ready to approve or promote.

***

## Trigger A New Training Run

See the [Configuring Events and Signal Templates](/guides/events) guide for instructions on defining signals, saving templates, and creating the next trained version.