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Model Dashboard

The Model Dashboard page provides users with a centralized and structured interface to view, manage, deploy, and monitor machine learning models on the Gesund.ai platform.

1. Header Navigation​

  • Breadcrumb Path: Home > Models
  • Tabs Available:
    • Models (currently selected)
    • Upload Models
    • Archived Models
    • Model Training

These tabs help users switch between viewing existing models, uploading new ones, reviewing archived models, or tracking training jobs.

2. Model List​

This section presents a dynamic list of all models available to the user. Each row includes key metadata:

  • Model Name: Indicates the model’s name and version (e.g., NeuroNet v2.0).
  • Model Status: Shows deployment status — either Deployed or Not Deployed.
  • Framework: Specifies the underlying framework used, such as PyTorch or TensorFlow.
  • Tags: Labels associated with each model, helping categorize by modality (e.g., CT, X-ray) or anatomy (e.g., Chest, Brain).

3. Model Actions​

Each model entry provides an action menu (typically via a three-dot icon), giving users quick access to essential management functions:

  • Unwatch: Remove the model from your watchlist.
  • Deploy: Launch the model into active use.
  • Stop: Terminate an ongoing deployment or related process.
  • Archive Model: Move the model to an archived state for record-keeping or inactive use.
  • Delete Model: Permanently delete the model from the platform (irreversible action).

4. Use Case​

This dashboard is ideal for both technical users (like ML engineers) and operations teams to:

  • Monitor model availability and status
  • Easily switch between different versions or architectures
  • Maintain a clean, organized workspace by archiving outdated or deprecated models

Additional Notes​

  • Users can filter and search models by name, status, or tags for easier navigation.
  • The platform ensures access control and permission management when performing critical actions like deploy or delete.