Welcome
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.
Marketing AION Engines
Marketing AION has a multi-stage pipeline as depicted below in the figure.

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.
  - Marketing AION Engines
    Marketing AION has a multi-stage pipeline as depicted below in the figure.
  - Accessing Marketing AION
    To access Marketing AION, complete the following steps:
- 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.
- 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.

Marketing AION Engines

Marketing AION has a multi-stage pipeline as depicted below in the figure.

A brief explanation of each stage follows:


INGESTOR - Data Ingestion	In this stage, the dataset is uploaded in Marketing AION GUI from disparate sources.
EXPLORER - Exploratory Data Analysis	This shows the details about the nature of data, the relation between features, model statistics, and performance information to derive descriptive insights.
TRANSFORMER - Data Processing	In this stage data-cleaning, data preparation, and outlier detectionares did automatically to improve the quality of data for better model accuracy.
SELECTOR - Feature Selection	The statistical analysis takes place to identify the relevant features for model training and remove the unimportant features based on correlation and importance.
LEARNER - Model Training Hyper Parameter Tuning	This stage trains configured models and selects the best parameters based on hyperparameter tuning (using which the models are trained). A broad spectrum of algorithms is supported.
PREDICTOR - Inference service	ML Model serving and inference services.
OBSERVER – Model Monitoring	It monitors the model for input and output drift of data or predictions.
CODER- Machine Learning as Code	It creates Python code automatically for ML pipeline components.

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