Welcome
Developer Guide
Building Usecases with API
To build a usecase or to train a model, complete the following steps:

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.
- Data Generator
- Building Usecases with API
  To build a usecase or to train a model, complete the following steps:
MLAC User Guide
User Guide

Building Usecases with API

To build a usecase or to train a model, complete the following steps:

Procedure

Generate unique tracking ID, copy it and paste it in a text editor.
Choose any one of the four ways to upload a dataset.
You get:
- Use Case ID
- Data Location
After uploading the dataset, use the issue spotter API to check for issues in the dataset like missing data, duplicate row, duplicate column, constant value, etc.
Start Training for the Use Case by passing the config file location stored at \Marketing AION_Dev_Mount_Point\HCLT\data\config\.
Based on your Model, execute Single Prediction or Batch Prediction.
Execute Monitoring Input Drift and Performance.
Execute Xplain Model Prediction
Your model is successfully trained.
Based on your requirement, perform any of the following sub-steps:
1. To check the status of API and to Update the status, access the API Status section.
2. To see the Usecases list, delete Usecases, or to view the logs, access the Usecases section.
3. To download Python Packages endpoint, download Docker Container endpoint, or to download MLAC endpoint, access the Download-model section.

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