Welcome
Developer Guide
Training
For Training, you can execute either of the generic training by using Train for pipeline API.

Release Notes
Installation Guide
Developer Guide
- Overview
  To access the Marketing AION REST API interface, use the Docker Desktop application to start the Docker container.
- Tracker
- API Status
  Use these APIs to get the status and set the status of the different types of API.
- Data Handler Overview
  Data Handler has four different APIs, and three of the APIs represent a different method uploading data.
- Training
  For Training, you can execute either of the generic training by using Train for pipeline API.
  - Config Training
    Use this API to get the configfile for specific problemtype which is used for the training of the model.
  - Training for Pipeline
    Use this API to train the model using the dataset and use case ID.
  - Retraining the Model
    Retraining is a crucial aspect of the model development lifecycle that involves updating an existing machine learning model to enhance its performance or adapt to new data.
  - Reconfiguring and Training the Model
    Reconfiguration and training are integral steps in the lifecycle of a machine learning model, allowing it to adapt to changing requirements, improve performance, and stay relevant.
  - Stop Training
    Use this API to stop the training of the model.
- Prediction
  The Prediction section lists APIs that are used for handling single prediction and batch prediction.
- Model Inference (beta)
- Download-model
  These APIs are used to download Python package, Docker container package, and Mlac.
- Monitoring
  Monitoring ML models checks for changes in data distribution ensuring that data is maintaining an acceptable level of variation.
- Xplain
  Explainability gives an explanation about Prediction and Model.
- Usecases
  Use the following APIs to retrieve the Use Case list, to delete use cases, and to retrieve the logs.
- EDA
- AutoPilot
  This Autopilot Mode refers to a feature that allows users to build an automated model without direct human intervention.
- Data Generator
- Building Usecases with API
  To build a usecase or to train a model, complete the following steps:
MLAC User Guide
User Guide

Training

For Training, you can execute either of the generic training by using Train for pipeline API.

Additionally, if you want different version for the same use case, you can retrain the model.

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