ML Observability
platform to Secure AI

Bring transparency & auditability to your Al Solutions that are acceptable by every stakeholder.

Try for freeGet a Demo

AryaXAI, the full stack ML Observability designed to adapt to new challenges in using 'AI'

ML Explainability
ML Monitoring
ML Auditing
Policy Controls

Provide detailed and diverse explanations

Feature Importance

Gain visibility into the parameters used for explainability. Get a complete overview of all the features in your data positively and negatively impacting  the prediction.

Similar Cases

Similar Cases use model explainability to figure out similar cases between the inference sample and training data for a list of similar cases. These ‘references as explanations’ can be a powerful source of evidence in interpreting model behaviours.

Observations

Provide a correlation between 'Domain Expertise' & 'Model functioning' in simple textual explanations.

Track, Debug & Resolve - Deliver reliability and consistency

Data drift

Data drift has a direct impact on model performance. Use 7+ techniques to detect data drifts.

Target Drift

Track Target/Model drifts and proactively debug the reason behind this from the UI.

Model Performance

Streamline performance monitoring by preemptively tracking model performance and course-correct usage. Gain complete context and visibility to evaluate model behaviour.

Bias/ Fairness

Identify bias in your models on any data point. Deliver compliant, ethical and socially responsible AI/ML.

Monitors/ Alerts

Configure customized alerts for Data drift, Target drift and Model performance. Fine-tune the frequency of alerts and get notified proactively on performance deviation

Programmatically automate storage, indexing and retrieval of ML artefacts

Artefacts

Perform in-depth audits around ML artefacts related to data, model development and validation. Define and execute audit cadences to record and capture key indexes during training and production.

Protect from AI uncertainty by enforcing risk policies on AI

Risk Controls

Define policy guardrails to enhance the applicability of the ML model across multiple teams and stakeholders. Ensure regulatory governance by administering customizable policy controls on data and model outcomes. 

Plug and usable XAI verticalization for your business

AryaXAI enhances the model predictions with domain-specific and diverse explanations, providing better acceptance, trust and transparency for your AI solutions.

Traditional MLOPs Output

Output
{'success': True,
 'details': {
 'status': 'completed',
 'pred_value': '0',
 'model_confidence': 0.999982238
}}

Use case: Loan Underwriting

AryaXAI Report
{'success': True,
 'details': {'status': 'completed',
  'pred_value': '0',
  'pred_category': '0',
  'feature_importance': {'last_fico_range_high': -2.54,
   'addr_state': -0.42,
   'mo_sin_old_rev_tl_op': -0.32,
   'max_bal_bc': -0.26,
   'revol_util': -0.26,
   'total_rev_hi_lim': -0.25,
   'earliest_cr_line': 0.24,
   'num_actv_bc_tl': -0.22,
   'num_bc_sats': 0.22,
   'dti': 0.22,
   'int_rate': 0.21,
   'fico_range_low': 0.2,
   'mo_sin_old_il_acct': -0.19,
   'installment': 0.17,
   'bc_util': -0.17,
   'num_rev_tl_bal_gt_0': 0.13,
   'bc_open_to_buy': 0.13,
   'term': -0.13,
   'total_il_high_credit_limit': -0.11,
   'num_sats': 0.1,
   'num_rev_accts': 0.09,
   'percent_bc_gt_75': 0.09,
   'open_act_il': 0.08,
   'num_bc_tl': 0.08,
   'mo_sin_rcnt_tl': -0.08,
   'revol_bal': -0.08,
   'il_util': 0.07,
   'issue_month': -0.07,
   'verification_status': -0.07,
   'mths_since_recent_bc': -0.06,
   'title': -0.06,
   'tot_hi_cred_lim': 0.06,
   'annual_inc': 0.05,
   'all_util': 0.05,
   'open_il_24m': 0.05,
   'pct_tl_nvr_dlq': 0.04,
   'total_bc_limit': 0.04,
   'loan_amnt': 0.04,
   'total_cu_tl': 0.03,
   'num_tl_120dpd_2m': -0.03,
   'mo_sin_rcnt_rev_tl_op': -0.03,
   'open_il_12m': 0.03,
   'avg_cur_bal': 0.03,
   'inq_last_6mths': -0.03,
   'inq_last_12m': 0.03,
   'purpose': 0.03,
   'open_acc': 0.03,
   'emp_title': -0.03,
   'num_tl_op_past_12m': 0.02,
   'num_il_tl': 0.02,
   'sub_grade': -0.02,
   'mths_since_recent_revol_delinq': -0.02,
   'mths_since_recent_inq': 0.02,
   'total_bal_ex_mort': -0.02,
   'funded_amnt_inv': -0.02,
   'num_op_rev_tl': -0.01,
   'mort_acc': -0.01,
   'emp_length': -0.01,
   'num_actv_rev_tl': -0.01,
   'mths_since_last_delinq': -0.01,
   'num_accts_ever_120_pd': -0.01,
   'open_acc_6m': -0.01,
   'mths_since_recent_bc_dlq': -0.01,
   'open_rv_24m': 0.01,
   'total_acc': -0.01,
   'initial_list_status': -0.01,
   'mths_since_rcnt_il': -0.01,
   'acc_open_past_24mths': -0.01,
   'total_bal_il': -0.01,
   'mths_since_last_major_derog': 0.01,
   'open_rv_12m': -0.01,
   'num_tl_90g_dpd_24m': 0.0,
   'tot_coll_amt': -0.0,
   'grade': 0.0,
   'tax_liens': 0.0,
   'pub_rec_bankruptcies': 0.0,
   'acc_now_delinq': 0.0,
   'funded_amnt': -0.0,
   'mths_since_last_record': -0.0,
   'num_tl_30dpd': 0.0,
   'application_type': -0.0,
   'pub_rec': 0.0,
   'delinq_2yrs': -0.0,
   'collections_12_mths_ex_med': 0.0,
   'inq_fi': -0.0,
   'fico_range_high': 0.0,
   'last_fico_range_low': 0.0,
   'chargeoff_within_12_mths': 0.0,
   'delinq_amnt': 0.0,
   'home_ownership': 0.0},
  'similar_cases': ['156676382',
   '127582413',
   '79741191',
   '79732795',
   '76213039',
   '146158012',
   '460848',
   '68232735',
   '154401097'],
  'observation_checklist': [{'success': True,
    'observation_name': '100% non-delinquent accounts',
    'observation_triggered': True,
    'observation_score': 0.04,
    'observation_statement': 'The applicant has never been delinquent across all accounts.',
    'statement_config': ['The applicant has never been delinquent across all accounts.']},
   {'success': True,
    'observation_name': 'Charged off in last 12m',
    'observation_triggered': False,
    'observation_score': 0.0,
    'observation_statement': 'The applicant previous loan was charged off in the last 12 month, a total of {chargeoff_within_12_mths}  incidents.',
    'statement_config': ['The applicant previous loan was charged off in the last 12 month, a total of {chargeoff_within_12_mths}  incidents.']},
   {'success': True,
    'observation_name': 'Delinquency in the past',
    'observation_triggered': False,
    'observation_score': 0.0,
    'observation_statement': 'The applicant had a delinquency in the past, a total of {acc_now_delinq} delinquencies.',
    'statement_config': ['The applicant had a delinquency in the past, a total of {acc_now_delinq} delinquencies.']},
   {'success': True,
    'observation_name': 'No charge offs',
    'observation_triggered': True,
    'observation_score': 0.0,
    'observation_statement': 'The applicant do not have any charge off in the last 12 months.',
    'statement_config': ['The applicant do not have any charge off in the last 12 months.']},
   {'success': True,
    'observation_name': 'No revolving account in 12m',
    'observation_triggered': False,
    'observation_score': 0.0,
    'observation_statement': "The applicant hasn't opened any revolving account in the last 12 months.",
    'statement_config': ["The applicant hasn't opened any revolving account in the last 12 months."]},
   {'success': True,
    'observation_name': 'No installment accounts yet',
    'observation_triggered': False,
    'observation_score': 0.0,
    'observation_statement': 'The applicant do not have any instalment accounts yet.',
    'statement_config': ['The applicant do not have any instalment accounts yet.']},
   {'success': True,
    'observation_name': 'Very few open accounts 12 months',
    'observation_triggered': False,
    'observation_score': 0.0,
    'observation_statement': 'The applicant has opened less than 2 accounts {num_tl_op_past_12m} ) in the past 12 months.',
    'statement_config': ['The applicant has opened less than 2 accounts {num_tl_op_past_12m} ) in the past 12 months.']},
   {'success': True,
    'observation_name': 'Very good Rev utilisation',
    'observation_triggered': False,
    'observation_score': 0.0,
    'observation_statement': 'The applicant has less than 5% revolving utilisation ({revol. {revol_util} ).',
    'statement_config': ['The applicant has less than 5% revolving utilisation ({revol. {revol_util} ).']},
   {'success': True,
    'observation_name': 'No negative public record',
    'observation_triggered': True,
    'observation_score': 0.0,
    'observation_statement': 'The applicant do not have any negative public record.',
    'statement_config': ['The applicant do not have any negative public record.']},
   {'success': True,
    'observation_name': 'Not delinquent at all',
    'observation_triggered': True,
    'observation_score': 0.0,
    'observation_statement': 'The applicant is never been delinquent.',
    'statement_config': ['The applicant is never been delinquent.']},
   {'success': True,
    'observation_name': 'Null IL account',
    'observation_triggered': False,
    'observation_score': 0.0,
    'observation_statement': "The applicant's instalment account utilisation is '0'.",
    'statement_config': ["The applicant's instalment account utilisation is '0'."]},
   {'success': True,
    'observation_name': 'Public bankruptcies',
    'observation_triggered': False,
    'observation_score': 0.0,
    'observation_statement': 'The applicant has {pub_rec_bankruptcies}  public bankruptcies.',
    'statement_config': ['The applicant has {pub_rec_bankruptcies}  public bankruptcies.']},
   {'success': True,
    'observation_name': 'Bad grade',
    'observation_triggered': False,
    'observation_score': 0.0,
    'observation_statement': 'The applicant has bad grade ie., {grade} .',
    'statement_config': ['The applicant has bad grade ie., {grade} .']},
   {'success': True,
    'observation_name': 'Was delinquent in the past',
    'observation_triggered': False,
    'observation_score': 0.0,
    'observation_statement': 'The applicant had a delinquency in the past. A total of {delinq_2yrs} in the past 2 years.',
    'statement_config': ['The applicant had a delinquency in the past. A total of {delinq_2yrs} in the past 2 years.']},
   {'success': True,
    'observation_name': 'Good FICO score',
    'observation_triggered': False,
    'observation_score': 0.0,
    'observation_statement': 'The applicant has good fico score. {fico_range_low} .',
    'statement_config': ['The applicant has good fico score. {fico_range_low} .']},
   {'success': True,
    'observation_name': 'Never delienquent for more than 120 days.',
    'observation_triggered': True,
    'observation_score': -0.01,
    'observation_statement': 'The applicant do not have any account with more than 120 days past the due.',
    'statement_config': ['The applicant do not have any account with more than 120 days past the due.']},
   {'success': True,
    'observation_name': 'Good grade',
    'observation_triggered': True,
    'observation_score': -0.02,
    'observation_statement': 'The applicant has good B ie., B.',
    'statement_config': ['The applicant has good grade ie., {grade}.']},
   {'success': True,
    'observation_name': 'Good FICO score',
    'observation_triggered': True,
    'observation_score': -2.34,
    'observation_statement': 'The applicant has a comparatively good FICO score. ie., 749.0 .',
    'statement_config': ['The applicant has a comparatively good FICO score. ie., {fico_range_high} .']}],
  'unique_identifier': '154983264',
  'tag': 'api',
  'created_at': '2023-03-09T16:26:20.784498+00:00'}}
Risk Policies
{'success': True,
   'details': {'status': 'completed',
   'policy_checklist': [{'policy_decision': '0',
    'success': True,
    'policy_name': 'Accounts with 120 delinquency',
    'original_decision': '0',
    'policy_statement': 'The applicant has {num_tl_120dpd_2m}  accounts where they were delinquent for more than 120 days.',
    'policy_triggered': False},
   {'policy_decision': '0',
    'success': True,
    'policy_name': 'Delinquent accounts with 90days',
    'original_decision': '0',
    'policy_statement': 'The applicant has {num_tl_90g_dpd_24m}  accounts which had more than 90 day delinquency.',
    'policy_triggered': False},
   {'policy_decision': '0',
    'success': True,
    'policy_name': 'Positive Tax Liens',
    'original_decision': '0',
    'policy_statement': 'The applicant has {tax_liens}  tax liens.',
    'policy_triggered': False},
   {'policy_decision': '0',
    'success': True,
    'policy_name': 'Confidence score for high FPs',
    'original_decision': '0',
    'policy_statement': 'Model confidence {model_confidence}) is less than the cut off threshold 90%',
    'policy_triggered': False},
   {'policy_decision': '0',
    'success': True,
    'policy_name': 'Not allowed to underwrite low FICO',
    'original_decision': '0',
    'policy_statement': 'This product requires FICO above 400. Modify the model prediction',
    'policy_triggered': False}],
    }}
Final Model Prediction
{'success': True,
   'details': {
   'status': 'completed',
   'pred_value': '0',
   'pred_category': '0',
   'final_decision': '0'
   }}

Traditional MLOps/ ML Experimentation

Output
{
    ‘prediction’ = 860,
    ‘class’ = 'Risk',
    ‘confidence’ = 0.86,
    ‘success’ = true
}
Processed by Arya XAI — Processed by Arya XAI — Processed by Arya XAI —

Use case: Other Usecase

AryaXAI Report
{
    ‘top features’ = {          
        ‘Feature 1’: ‘0.02’,
        ‘Feature 2':…},
    ‘Observations’ = {
        ‘Very high enquiries in the recent times’: ‘0.23'}
}
Risk Policies
{
    ‘top features’ = {          
        ‘Feature 1’: ‘0.02’,
        ‘Feature 2'…},
    ‘Observations’ = {
        ‘Very high enquiries in the recent times’: ‘0.23'}
}
Final Model Prediction
{
    ‘top features’ = {          
        ‘Feature 1’: ‘0.02’,
        ‘Feature 2'…},
    ‘Observations’ = {
        ‘Very high enquiries in the recent times’: ‘0.23'}
}
Arrow Right Icon
Arrow Right Icon

Custom templates as per your need

AryaXAI provides low-code customizations such that any stakeholders can participate, contribute and modify these layers for better success of the AI solution.

All-in-one visibility to build trusted models

Deliver true-to-model explanations accurately and consistently to any stakeholder.

Performance monitoring on a single source

Get deeper insights into model performance. Prevent decay in model performance by scrutinizing your models for irregularities.

Deploy Responsible AI processes at scale

Deliver consistency and trust to end users through transparent, inclusive AI models

AryaXAI integrates with
your ML Stack

H2O.ai

XGboost

Pytorch

AWS sagemaker

databricks

MLflow

jupyter

Google ML Cloud

Sci-Kit Learn

Colab

weights and biases

Tensorflow

Keras

ONNX

Azure ML

datarobot

AWS sagemaker

Pytorch

XGboost

H2O.ai

jupyter

MLflow

databricks

Colab

Sci-Kit Learn

Google ML Cloud

weights and biases

Tensorflow

Keras

datarobot

Azure ML

ONNX

AWS sagemaker

databricks

MLflow

jupyter

ONNX

Azure ML

datarobot

H2O.ai

XGboost

Pytorch

weights and biases

Tensorflow

Keras

Google ML Cloud

Sci-Kit Learn

Colab

datarobot

H2O.ai

Colab

ONNX

Pytorch

Keras

jupyter

databricks

AWS sagemaker

XGboost

MLflow

Azure ML

weights and biases

Tensorflow

Sci-Kit Learn

Google ML Cloud

Enterprise ready form day one!

Full stack ML Observability

It offers all key observability components in one place. Allows easy participation and sharing of information across stages and stakeholders.

Get Started in few mins

Using our APIs & SDKs, it is quite easy to get started with AryaXAI. With an easy-to-use GUI, users can go live in a jiff. DIY rocks!

Troubleshoot quickly and precisely

With state-of-the-art ML monitoring tools, you can precisely identify the issues in your models and get insights on resolutions.

Highly scalable in your preferred environment

It is highly scalable and flexible and can be scaled to millions of predictions in the preferred environment.

Operations Across Industries

ML Observability is critical to various use cases and various stakeholders.

Banking

Use case: Credit Underwriting for secure/unsecured loans

Insurance

Use Case: Life & Health Insurance Underwriting

Financial Services

Use Case: Identifying fraud/suspicious transactions

General

Use Case: Product recomendation

Manufacturing

Use Case: Failure prediction in continuous manufacturing

Autonomous Cars

Use Case: Autonomous Cars on road

Resources

Unlock more resources on AI Governance

Access the resource repository that includes blogs, research papers, white papers and events related to the latest developments in AI Governance, AI audit, ML monitoring, Explainable AI and much more.
EU AI Act is here: How can organizations stay compliant

EU AI Act is here: How can organizations stay compliant

The world's first comprehensive AI law the European Artificial Intelligence Act (AI Act) entered into force on 1st August 2024. The act establishes a comprehensive framework for the responsible and ethical development, deployment, and use of AI technologies.
Read the article
No items found.
Find more on XAI Knowledge Hub

Let's talk.

Schedule a demo on how AryaXAI can deliver AI Governance, transparency and acceptance of AI solutions to scale with confidence.

Schedule a demo

By clicking “Accept All”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.

DenyAccept All