Models

Manage your trained models with versioning and metadata.

Models View

Registering a Model

Navigate to Deep Learning Platform → Models → Click Create

Create Model Form

Basic Information

Model Name* (Required)

• Enter a descriptive name for the model

• Example: resnet50-imagenet, bert-sentiment

Version* (Required)

• Semantic version (major.minor.patch)

• Default: 1.0.0

• Helper text: "Semantic version (major.minor.patch)"

Description (Optional)

• Detailed description of the model

Model Configuration

Framework* (Required)

• Select framework from dropdown:

  • PyTorch

  • TensorFlow

  • ONNX

  • Scikit-learn

  • Others

Task Type* (Required)

• Select task type:

  • Classification

  • Regression

  • Detection

  • Segmentation

  • Others

• Default: classification

Model Type* (Required)

• Select model architecture:

  • Custom

  • ResNet

  • BERT

  • YOLO

  • Others

• Default: custom

Status* (Required)

• Select model status:

  • Draft

  • Training

  • Completed

  • Deployed

  • Archived

• Default: draft

Metadata

Tags (Optional)

• Comma-separated tags for organization

• Example: production, v1, optimized

Author (Optional)

• Model creator or team name

License (Optional)

• Select license from dropdown:

  • MIT

  • Apache 2.0

  • GPL

  • Proprietary

  • Others

Public Access (Checkbox)

• Make model accessible to all organization members

Actions

• Cancel: Discard and close

• Create Model: Submit and register the model

Example Configuration

Model Name: resnet50-imagenet-v1
Version: 1.0.0
Description: ResNet50 trained on ImageNet-1K, 76.5% top-1 accuracy
Framework: PyTorch
Task Type: Classification
Model Type: Custom
Status: Draft
Tags: computer-vision, production
Author: ML Team
License: MIT
Public Access: ✓

Viewing Model Details

To view detailed information about a model:

1. Navigate to Deep Learning Platform → Models

2. Click on a model from the list

3. View comprehensive details in the modal dialog

View Model Details

Details Panel Sections:

Basic Information:

• Model Name: e.g., "XGBoost House Prices"

• Version: Semantic version (e.g., 1.0.3)

• Description: Full description of the model and its purpose

Model Configuration (First Section):

• Model Name: Display name

• Version: Current version number

• Description: Detailed model description

Model Configuration (Second Section):

• Framework: XGBoost, PyTorch, TensorFlow, etc.

• Task Type: Regression, Classification, etc.

• Model Type: Custom, ResNet, BERT, etc.

• Status: Training, Draft, Completed, Deployed

Metadata:

• Tags: Comma-separated tags for organization

• Author: Model creator or team name

• License: Model license information

Editing a Model

To update model information:

1. Open model details page

2. Click Edit button (or three-dot menu → Edit)

3. Modify editable fields in the Edit Model modal

Edit Model Form

1. Click Update Model to save changes

[!NOTE] The Edit form is identical to the View form, but with editable fields and an "Update Model" button.

Editable Fields:

• ✅ Description

• ✅ Tags

• ✅ Status (Draft, Training, Completed, Deployed, Archived)

• ✅ Author

• ✅ License

• ✅ Public Access

• ❌ Model Name (cannot edit)

• ❌ Version (create new version instead)

• ❌ Framework (cannot edit)

Creating a New Version

To create a new version of an existing model:

1. Open model details

2. Click New Version button

3. Enter new version number (e.g., 1.1.0 → 1.2.0)

4. Upload new model artifacts

5. Update description and metrics

6. Click Create

Version Guidelines:

• Major (2.0.0): Breaking changes, new architecture

• Minor (1.1.0): Improvements, new features

• Patch (1.0.1): Bug fixes, minor updates

Downloading Model Artifacts

To download model files:

1. Open model details

2. Navigate to Artifacts section

3. Click Download on desired files:

   • Model weights (.pt, .h5, .onnx)

   • Configuration files

   • Tokenizers/preprocessors

   • README and documentation

4. Files will be downloaded to your local machine

Deleting a Model

To remove a model:

1. Navigate to model details

2. Click Delete button

3. Confirm deletion

[!WARNING] You cannot delete a model that is currently deployed. Stop all deployments first.

Before Deleting:

• Check for active deployments

• Download artifacts if needed

• Update dependent systems

• Archive instead of delete if uncertain

Model Lifecycle Management

Model Stages:

1. Development: Under active development

2. Staging: Ready for testing

3. Production: Deployed to production

4. Archived: No longer in use

Model Versioning Best Practices

Version Numbering:

• 1.0.0: Initial production release

• 1.1.0: New features, improved accuracy

• 1.1.1: Bug fix, no architecture change

• 2.0.0: New architecture, breaking changes

Next Steps

• Deploy your model via Deployments

• Link to training Experiments

• Monitor in Analytics