Welcome
Developer Guide
Data Handler Overview
Data Handler has four different APIs, and three of the APIs represent a different method uploading data.
Issue Spotter
Use this API to detect issues in datasets like missing data, duplicate row, duplicate column, constant value, etc. To detect issues, you must provide the file path.

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.
  - Uploading Data by Browser
    Use this API endpoint to upload data and initiate usecase generation. The data is acquired through browsing file from the system for training. It provides functionalities to handle data uploads, generate usecases, and update API statuses. It allows creation of a derived column based on two existing columns and an operation (Add, Subtract, Multiply, Divide). It also handles data type validation to ensure operations are performed on integer columns. For additional information, see the attached README file.
  - Uploading Data from NiFi
    Use this API endpoint allows to upload data and initiate usecase generation. The data is acquired from NiFi (an integrated data logistics platform that automates the flow of data between systems.) for training. It provides functionalities to handle data uploads, generate usecases, and update API statuses.
  - Uploading Data from URL
    This API endpoint allows you to upload data and initiate usecase generation. The data for training is acquired from an URL hosting the dataset. It provides functionalities to handle data uploads, generate usecases, and update API statuses.
  - Issue Spotter
    Use this API to detect issues in datasets like missing data, duplicate row, duplicate column, constant value, etc. To detect issues, you must provide the file path.
- 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

Issue Spotter

Use this API to detect issues in datasets like missing data, duplicate row, duplicate column, constant value, etc. To detect issues, you must provide the file path.

Method

Endpoint

/v1/aion/issue_spotter/

Request Parameters


tracking_id	Mandatory	Use the tracking_id received after executing the Generating Unique Tracker ID API.
file_name	Mandatory	The location received in the response of Uploading Data by Browser API.

After providing the values, click Execute.

Response Parameters

After clicking Execute, you get the following values in the Response body:

status
msg

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