Welcome
User Guide
Model Training
Training the Model
Model training tab gives the information about the Problem type, Selected algorithms, number of rows and Training Features, Training Data, Data balancing and Number of iterations.

Release Notes
Installation Guide
Developer Guide
MLAC User Guide
User Guide
- Introduction
  Marketing AION (Artificial Intelligence ON) is an AI life cycle management platform for applying machine learning to real-world problems. Marketing AION 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
  - Data Upload
    Data upload tab is used to load the dataset in the GUI to train the model and get insight of it.
  - Basic Configuration
    This section provides the option to select more than one algorithm based on problem type and also provides the option to discard the unnecessary features from the dataset.
  - Updating Advanced Configuration
    To update the Advanced Configuration, complete the following steps:
  - Training the Model
    Model training tab gives the information about the Problem type, Selected algorithms, number of rows and Training Features, Training Data, Data balancing and Number of iterations.
    - Details of the Trained Model
      After successful training, the trained model details can be viewed.
- 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.
- 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.

Training the Model

Model training tab gives the information about the Problem type, Selected algorithms, number of rows and Training Features, Training Data, Data balancing and Number of iterations.

About this task

The data balancing is applicable only for classification problem. The percentage of test data can be changed accordingly by the user. Number of iterations reflects the size of hyper-parameter space selected by the user.

Procedure

Click Train Model tab to train the model.
Training can be aborted, if required, by clicking Stop Training tab.
Model training can also be performed by using API. For more information, see Fairness Metrics.

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