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.. _amldashboard_specification:

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AML Dashboard
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The *AML Dashboard* serves as a user-friendly tool for training, evaluating, and applying |aml| models to several datasets.
Built upon the `Marcelle <https://marcelle.dev/>`__ framework, the dashboard offers standard interaction :term:`GUI`\s and the ability to create custom interactive widgets compatible with Marcelle's structure.

The primary goal of the *AML Dashboard* is to enable users to:

1. Collect personal gesture data or load existing datasets.
2. Train classifiers using the *AML Engine*.
3. Compare the performance of |aml| models with other :term:`ML` approaches.

To achieve this, the *AML Dashboard* integrates seven main interfaces:

* **Dataset Interface**: Allows users to collect data through personal gestures or load existing datasets.
* **Training Interface**: Facilitates the training of |aml| models based on loaded datasets.
* **Model Exploration Interface**: Enables users to explore and compare the capabilities of |aml| models with other :term:`ML` approaches.
* **Real Time Exploration Interface**: Applies the trained |aml| algorithm in real-time. This feature is only available when the dataset chosen is ``Sensors``. It enables users to explore new sound patterns using personal gestures.
* **Model Fetching Interface**: Fetches trained models and statistics using an AML-IP Collaborative Learning scenario.
* **Context Broker Interface**: Interacts with the Context Broker to handle inference data.
* **AML-IP Management Interface**: Manages the AML-IP nodes, allowing users to start, stop, and delete the nodes as well as monitor their status.

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