AI/ML Workbench
A no-code machine learning environment for generating predictive insights from process data. Build, train, validate, and deploy predictive models without data science expertise.
Access via the AI/ML Workbench icon from the main navigation. Common use cases include: predicting the next activity, identifying process violations, forecasting SLA breaches, predicting process delays.
Creating a Predictive Model
Click Create. Model creation has three steps:
- General: Provide Model Name and select the Event Log (only Event Logs from the Event Log Upload module are available).
- Training: Select Predictor Type (Next Activity), Input Variables (Activity Name, User, Department, Vendor, Region, etc.), arrange activities via drag-and-drop, set Testing Data Size (0.0–1.0), and select Algorithm.
- Validation: Review all configurations. Click the Edit icon to modify any section, then click Save Model.
ℹ️ The selected Event Log cannot be modified after model creation begins.
Available Algorithms
| Algorithm | Notes |
|---|---|
| Decision Tree | Transparent, easy to interpret |
| Random Forest | Generally strong default choice for most process prediction scenarios |
| XGBoost | High performance for structured data |
| K-Nearest Neighbors (KNN) | Good for pattern-based predictions |
Executing a Model
Creating a model only saves its configuration. To begin training: go to List of Models → click Edit for the desired model → click Execute.
Reports
Once execution completes, access Reports via Actions → Report. Available tabs:
- Classification Report – Accuracy, Precision, Recall, F1 Score, Support.
- Predictions – For each case: current process state, predicted next activity, prediction results.
Click Download to receive reports via email.
Model Versioning
Create multiple versions to experiment with different configurations. To create a new version: Edit an existing model → modify configuration → Save and Execute. Previous versions are preserved for comparison.