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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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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

Prompting

Following the model's training phase, before synthetic data generation, AryaXAI offers a 'Prompting' feature that allows you to establish specific conditions for the data generation process.

List existing prompts:


project.get_synthetic_prompts()

Create Synthetic Prompts


project.create_synthetic_prompt(
    name='Grade A synths',
    expression='(grade = A)'
)

Check configuration of prompts:


project.synthetic_prompt(prompt_id='2666f1f8ec1c')

Make prompts active/inactive:

Deactivate or delete the prompt. An 'Active' status indicates that the conditions specified in the prompt will be applied during the generation of new synthetic data.


project.update_synthetic_prompt(prompt_id='2666f1f8ec1c',status='inactive')

Additional functions:


# prompt.get_expression()

Prompting

Following the model's training phase, before synthetic data generation, AryaXAI offers a 'Prompting' feature that allows you to establish specific conditions for the data generation process.

List existing prompts:


project.get_synthetic_prompts()

Create Synthetic Prompts


project.create_synthetic_prompt(
    name='Grade A synths',
    expression='(grade = A)'
)

Check configuration of prompts:


project.synthetic_prompt(prompt_id='2666f1f8ec1c')

Make prompts active/inactive:

Deactivate or delete the prompt. An 'Active' status indicates that the conditions specified in the prompt will be applied during the generation of new synthetic data.


project.update_synthetic_prompt(prompt_id='2666f1f8ec1c',status='inactive')

Additional functions:


# prompt.get_expression()
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
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