Welcome
User Guide
Trusted AI
This feature is available for classification and regression problem types.

HCL MaxAI Workbench Documentation
System Requirements
Release Notes
Installation Guide
Developer Guide
MLAC User Guide
User Guide
- Introduction
  MaxAI Workbench is an AI life cycle management platform for applying machine learning to real-world problems. MaxAI Workbench encompasses the complete pipeline from raw data ingestion to a deployable machine learning model with a less-code/no-code approach.
- Model Management
- Model Training
- Model Prediction
  Model prediction predicts the input data based on the pattern of training data.
- Trusted AI
  This feature is available for classification and regression problem types.
  - Fairness Metrics
    Fairness metrics are a set of measures that enables the user to detect the presence of bias in model.
  - Performance
    Model performance is an assessment of the model's ability to perform a task accurately not only with training data but also in real-time with runtime data when the model is deployed.
  - Transparency
    Transparency helps users to understand the input data, algorithm and expected output of an ML model.
  - Explainability
    Explainability tab gives an explanation about Prediction and Model.
  - Robustness
    Robustness of AI/ML models is analyzed by Sensitivity Analysis examining the extent to which the results are affected by changes in input data.
- Model Monitoring
  This feature is available for classification and regression problem types.
- Utility
  For Data Generator and BYOM (Bring Your Own Model), select the Utility tab.
- Preconfigured Reference Models
  MaxAI Workbench provides reference models for use with products like Unica Campaign, Unica Journey, HCL CDP, etc.

Trusted AI

This feature is available for classification and regression problem types.

Trusted AI provides an integrated approach to evaluate, quantify, and monitor the impact and trustworthiness of the models.