Model Version

1. Add Model Versions
2. Add Model variations
3. Add Model variations Tags
4. Delete Model variations Tags
5. Model variation Metadata
6. Delete Model variation
7. Remove Model Version

insert_linkModel Versions vs. Model Variations

insert_linkModel Version

1. A model version represents a specific iteration or generation of a model. It is typically a snapshot of the model at a particular point in its lifecycle, often marked by significant changes or improvements made to the model's architecture, training data, or hyperparameters. Each model version encapsulates the complete setup needed to replicate the model's performance.
2. Example: Consider a facial recognition model. The initial release, trained on a limited dataset, is designated as Version 1.0. Over time, as the training dataset is expanded with more diverse images, the model is retrained to improve accuracy and robustness, resulting in Version 2.0.
3. Use Case: Model versions are used to track the evolution of a model over time. This allows developers and data scientists to deploy specific versions based on performance, compliance with regulations, or compatibility with specific applications.

insert_linkModel Variation

1. A model variation refers to different configurations or customizations of a given model version within a specific framework. Variations might include changes in configuration settings, use of different layers or activation functions, or adjustments specific to deployment environments. Each variation within a version/framework combination is identified by a unique variation name.
2. Example: Continuing with the facial recognition model in Version 2.0, you might have different variations to address different operational needs:
• Variation A: Configured for real-time processing on mobile devices, with a focus on speed over accuracy.
• Variation B: Optimized for high accuracy in controlled environments, suitable for security systems.
3. Use Case: Model variations allow for fine-tuning and optimizing a model version to meet specific requirements or constraints of different deployment scenarios. They enable organizations to leverage the same underlying model architecture and training, while adapting to diverse needs and conditions.

insert_linkSupported Frameworks

1. A model version can have multiple frameworks, and each framework might consist of multiple variations with different configurations or customizations.
2. The platform currently facilitates a few frameworks, with additional ones currently in development and slated for imminent release. The frameworks list is:
• Pytorch
• Keras - Tensorflow
• Scikit Learn [Coming Soon]
• Transformers [Coming Soon]
• Fastai [Coming Soon]
• LightGBM [Coming Soon]
• XGBoost [Coming Soon]
• Prophet [Coming Soon]

insert_linkAdd model versions

Add versions using client library:

Please note, the creation of model versions is supported solely via our proprietary library or client library tool.

insert_linkAdd model variations

Add variations using client library:

Please note, the creation of model variations is supported solely via our proprietary library or client library tool.

insert_linkAdd model variation tags

1. Under the Model Version section, expand the Model Variation Configuration section.
2. Click on Manage tags

3. To add multiple tags, use the Add tag button. If any tag is not needed you can delete it by clicking on the delete button on the right side of the tag.

4. Finally, click on Submit to add the tags in a model.

insert_linkRemove model variation tags

1. Once the tags are removed from the model variation, you will no longer be able to categorize, search or filter that variation in relation to the tag key or values. Before deleting the tags make sure you no longer need those tags.
2. To remove model tags, simply click on the cross icon (X) of a tag.

3. To remove all model tags at once, click on the Remove all tags button.
4. Confirm the removal process and click on Confirm

insert_linkModel variation metadata

insert_linkDelete Model Variation

1. Select the variant to delete from the dropdown. Then click on the Delete variant button.

2. Confirm the deletion process and click on Confirm.

insert_linkDelete Model Version

1. Select the version to delete from the dropdown. Then click on the Delete version button.

2. Confirm the deletion process and click on Confirm