SDK
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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

Model summary

Once your model is successfully trained, AryaXAI provides users with a comprehensive list of all models, offering an overview and management capabilities. These details include:

  • Model name and status
  • Creation and updation details
  • Accuracy, precision, recall, and F score metrics

Get Active model details:


modelinfo = project.model_summary()
modelinfo.info()

Data stability

Within AryaXAI, you have the capability to track data drifts and receive notifications upon detecting any identified drift in your data.

Data drift occurs when the characteristics of the data encountered by a model in production deviate significantly from those of the dataset on which the model was trained.

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'])

Model summary

Once your model is successfully trained, AryaXAI provides users with a comprehensive list of all models, offering an overview and management capabilities. These details include:

  • Model name and status
  • Creation and updation details
  • Accuracy, precision, recall, and F score metrics

Get Active model details:


modelinfo = project.model_summary()
modelinfo.info()

Data stability

Within AryaXAI, you have the capability to track data drifts and receive notifications upon detecting any identified drift in your data.

Data drift occurs when the characteristics of the data encountered by a model in production deviate significantly from those of the dataset on which the model was trained.

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'])
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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