SDK
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 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

View model details

Get details of Active model:


modelinfo = project.model_summary()

To fetch Model Performance of Active Model, use the following function:


project.get_model_performance()

To remove a model:


project.remove_model('model_name')

Get current data config details. If you don't modify the data settings, any future fine-tuning will use the same data settings


modelinfo.data_config()

Activate/Deactivate

Activate any model by simply passing the model name. The active model will be used to run any inferencing - both in API, GUI or through batch files.


project.activate_model('model_name')  # make any model active

View model details

Get details of Active model:


modelinfo = project.model_summary()

To fetch Model Performance of Active Model, use the following function:


project.get_model_performance()

To remove a model:


project.remove_model('model_name')

Get current data config details. If you don't modify the data settings, any future fine-tuning will use the same data settings


modelinfo.data_config()

Activate/Deactivate

Activate any model by simply passing the model name. The active model will be used to run any inferencing - both in API, GUI or through batch files.


project.activate_model('model_name')  # make any model active
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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