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QUICKSTART

Introduction
The Schema

GETTING STARTED

Signing up for AryaXAI
Signing up for AryaXAI
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Manage Organizations
Manage Organizations
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Manage users
Manage users
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Manage workspace and projects
Manage workspace and projects
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Manage Files
Manage Files
Add Data
Add Models
Manage Compute
Manage Compute
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COMPONENTS

Project Summary
Project Summary
Data Diagnostics
Data Diagnostics
Model Diagnostics
Model Diagnostics
ML Models
ML Models
ML Explainability
ML Explainability
ML Monitoring
ML Monitoring
ML Risk Policies
ML Risk Policies
Synthetic AI
Synthetic AI

TUTORIALS

Data Summary

Get Data Summary:


project.data_observations('Training') # Can pass any Tag for getting Summary

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Initiate new Data Diagnostics

You can retrieve the data warning specifically by 'tag'. Just pass the 'Tag' below.


project.data_warnings('Training')

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Compute selection

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Default data drift report generated after uploading train and test data


project.data_drift_diagnosis()

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Seeing Data Drift report between Tags


project.data_drift_diagnosis(['Training'],['Testing'])

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Data alerts

The Data Warning section highlights any inconsistencies detected in the uploaded analytical data. These warnings encompass various issues, including missing data, high feature correlation, high cardinality, and more.

You can also retrieve the data warning specifically by 'tag'. Just pass the 'Tag' below.


# data diagnosis
project.data_warnings('Training')

Data Summary

Get Data Summary:


project.data_observations('Training') # Can pass any Tag for getting Summary

‍

Initiate new Data Diagnostics

You can retrieve the data warning specifically by 'tag'. Just pass the 'Tag' below.


project.data_warnings('Training')

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Compute selection

‍

Default data drift report generated after uploading train and test data


project.data_drift_diagnosis()

‍

Seeing Data Drift report between Tags


project.data_drift_diagnosis(['Training'],['Testing'])

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Data alerts

The Data Warning section highlights any inconsistencies detected in the uploaded analytical data. These warnings encompass various issues, including missing data, high feature correlation, high cardinality, and more.

You can also retrieve the data warning specifically by 'tag'. Just pass the 'Tag' below.


# data diagnosis
project.data_warnings('Training')
Example H2
Example H3
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Serverless ML
Dedicated
Signing up for AryaXAI
Add Data
Introduction
Bring your own models (BYOM)
Project Summary
Introduction to ML Monitoring
Introduction to Synthetic AI
Introduction to ML Explainability
Data Drift
The Schema
Global Explanations
Manage Organizations
Synthetic Model
Add Models
Build/Fine-tune Models: AutoML
Data Diagnostics
Target Drift
Observations as explanations
Synthetic Data generation
Manage Models
Manage users
Model Diagnostics
Case wise inferencing & Explainability
Bias Monitoring
Train model
Privacy testing
Manage workspace and projects
ML Models
Inferencing
ML Explainability
Model Performance
Prompting
ML Monitoring
Monitors and Alerts
Manage Files
ML Risk Policies
Manage Compute
Synthetic AI
Company
Resources
Industries

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