- Added the “compact modeling” framework, enabling end-users to avoid writing boilerplate code (e.g., data retrieval, performance analysis) when preparing models. This now supports PyTorch and TensorFlow.
- Added capability for producing customizeable real-time training update graphs for TensorFlow and PyTorch modeling. Other graphs (e.g., precision-recall) have been added as well.
- Added interactive confusion matrix capabilities, enabling detailed view of mis-labeled testing data, etc.
- Added capability to evaluate output of trained models in the JedAI Unity client via the explainability tab. Works for TensorFlow and PyTorch tabular, image and text classification as well as regression models.
- Expanded use of “model_type” parameter to include “tabular_classification,” alleviating need to perform guesswork in code to determine model type.
- Refactored/improved retrieval code for virtual datasets and predictions to consider differences between TensorFlow and PyTorch data.
- TensorFlow models must now return all four of ordered_class_names, ordered_feature_names, input_name and output_name with compact modeling.
- PyTorch models must return ordered_class_names with compact modeling.
- Code refactoring to fix input and output mappings with TensorFlow models
- Various other bugfixes
- Miscellaneous code refactoring to make PyTorch modeling less error-prone.
- Enablement of ElasticSearch upgrades, minor related bugfixes