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

> ## Agent Instructions
> Choose User guide for browser tasks, Developers for CLI, SDK and external agents, and Administration for organization management.
> Confirm identity and organization/project context before operations. Documentation access does not grant ThinkPlatform permissions.
> Review side effects, verify changes with a read, and do not blindly retry an operation with an uncertain outcome.
> Use documented UI steps for the web app and documented public CLI or SDK interfaces for automation. Follow each page’s prerequisites and authentication requirements.

# Training and models

> Inspect training bundles and model assets in your project.

Use **Training** and **Models** to work with the training and model resources available in your project. The available features depend on configured services and permissions.

## Before you begin

Select the intended organization and project. Obtain access to the relevant training or model resources. Before deploying a model or starting a training operation, review its inputs, destination, and potential resource use.

## Inspect and upload a training bundle

1. Open **MLOps → Training → Bundles** and select an available bundle and version to inspect its manifest, parameter schema, command preview, and files.
2. To add a bundle, use **Upload** if available. Supply a version and select a ZIP or folder whose root contains `training-bundle.yaml`.
3. Choose **Upload** and wait for the result. Open the uploaded version and verify its details and files.

Uploading a bundle makes it available for use; it does not itself establish that training has run.

To choose a dataset and run training, follow [Run a training job](https://docs.thinkplatform.ai/guides/data/training-jobs.md).

## Inspect models

1. Open **MLOps → Models → Registry** and select the relevant model and version.
2. Review the displayed model details, artifacts, and associated training information where available.
3. If deployment controls are available and you are authorized to use them, open **MLOps → Models → Deployments** and review the target and confirmation details before deploying. Check the resulting deployment status and destination. See [Deploy a model](https://docs.thinkplatform.ai/guides/data/model-deployment.md) for cloud and edge workflows.

## Troubleshoot

Check the selected project and permissions if resources are missing. For an upload error, verify the bundle structure and version and read the displayed message. For a failed training or deployment operation, inspect its status and error before retrying; confirm whether any changes already took effect.


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