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

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

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.

Method

POST

Endpoint

/v1/aion/retrainModel/

Request Parameters


use_case_name	Mandatory	The ID of the use case that you want to retrain
model_version	Mandatory	The version number of the model.

After providing the values, click Execute.

Response Parameters

After clicking Execute, you get a Response similar to the following example:

Config file location
Data file location

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