Welcome
Developer Guide
AutoPilot
This Autopilot Mode refers to a feature that allows users to build an automated model without direct human intervention.

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.
- 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.
  - File Upload
  - AutoPilot for Classification
    Use this API, if you want to upload the dataset that you want to train the model with. If you use the classification endpoint, then the data you upload should be relevant to the problem type and the same applies for regression.
  - AutoPilot for Regression
- Data Generator
- Building Usecases with API
  To build a usecase or to train a model, complete the following steps:
MLAC User Guide
User Guide

AutoPilot

This Autopilot Mode refers to a feature that allows users to build an automated model without direct human intervention.

This does not allow direct human intervention in selecting any targets or other parameters as such and thus democratize the entire process.

The approach aims to simplify model building, making it accessible to individuals of all technical backgrounds, thereby fostering ease of adoption.

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