GUI
QUICK LINKS

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 Compute
Manage Compute
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Manage Tasks
Manage Tasks
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COMPONENTS

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

TUTORIALS

Data diagnostics

Once you upload data, AryaXAI automatically performs a comprehensive analysis of the added datasets for your initial file. This analysis includes data profiling, data modeling, and explainability. You can easily perform these tasks manually at a later stage if needed.

Initiate new data diagnostics

Selecting the 'Refresh Data' tab located at the top right corner of the interface invokes the data profiling task. This will display a dropdown menu where you can select the desired compute server. After choosing the appropriate compute server, click to refresh your data. 

Within the Data Diagnostics tab, AryaXAI also offers an overview of the total data volume, unique features and Warnings.

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.

Example H2
Example H3
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Introduction
Project Summary
Signing up for AryaXAI
Add Data
Data settings
Bring your own models (BYOM)
Introduction to ML Explainability
Introduction to ML Monitoring
Introduction to Synthetic AI
Data Drift Monitoring
The Schema
XAI: Similar Cases
Manage Organizations
Data diagnostics
Data addition from API
Build/ Fine-tune Models
Synthetic Models
Model Diagnotics
Observations as explanations
Target Drift Monitoring
Manage users
Add Models
Manage Models
Synthetic data generation
Manage workspace and projects
ML Models
Model Summary
Case wise inferencing & explainability
Bias Monitoring
Privacy testing
ML Explainability
Model Performance Monitoring
Inferencing
Prompting
ML Monitoring
Manage Files
Monitors and Alerts
ML Risk Policies
Manage Compute
Synthetic AI
Manage Tasks
Company
Resources
Industries

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