Data Science Workshop (DSW) supports a range of machine learning workflows — from model training and evaluation to deployment and responsible AI analysis. The following use cases show how to apply DSW to common ML tasks.
Responsible AI
Use responsible-ai-toolbox in DSW to analyze model behavior and identify potential issues before deployment.
| Use case | Description |
|---|---|
| Responsible AI: Fairness analysis | Evaluate fairness of an income-prediction model (annual income > 50K) across gender and race using responsible-ai-toolbox. |
| Responsible AI: Error analysis | Identify and diagnose prediction errors in an income-prediction model using responsible-ai-toolbox. |
Computer vision
| Use case | Description |
|---|---|
| Object detection with EasyVision | Detect objects in images using EasyVision in DSW. |
Natural language processing
| Use case | Description |
|---|---|
| Text classification with EasyTransfer | Train and evaluate a text classification model, run predictions, export model files, and deploy the model using EasyTransfer in DSW. |
Speech
| Use case | Description |
|---|---|
| Speech recognition with EasyASR | Use EasyASR for speech recognition in DSW. |
| Speech classification with EasyASR | Use EasyASR for speech classification in DSW. |