Unlock Speed, Scale, and Depth in AI/ML Analytics

MoDeVa: Elevate Your Model Development, Validation, and Testing

In today’s AI-driven landscape, ensuring that models are robust, interpretable, and scalable is more critical than ever. MoDeVa (Model Development and Validation) provides a comprehensive suite of tools to support advanced AI/ML Analytics from development and validation to monitoring. Whether you are building AI/ML models from scratch or validating complex statistical frameworks, MoDeVa empowers data scientists and risk teams with unparalleled interpretability, explainability, rigorous testing and auditability.

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Key Modules

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Conceptual Soundness

MoDeVa ensures that models are built on a strong foundation by enabling:

Data Quality Suitability – Advanced exploratory data analysis, outlier detection, and data drift testing. 
Feature Selection Causality – Identify the most relevant features using permutation-based methods and causal testing. 
Interpretable Model Development – Supports advanced frameworks like FANOVA, GAMI-Net, Neural Trees, and Mixture-of-Experts (MoE).

Outcome Analysis

Gain insights into model performance & weaknesses with:

Performance Metrics Residual Analysis – Diagnose errors and overfitting across model segments. 
Reliability Testing – Evaluate prediction uncertainty. 
Robustness Testing – Evaluate stability under noisy inputs and adversarial conditions. 
Resilience Testing – Measure model performance degradation under real-world distribution shifts. 
Fairness Bias Auditing – Conduct fairness analysis and implement bias mitigation strategies.

Model Explainability

Enhance model transparency with:

Exact Interpretability – Global and local interpretability for MoDeVa native ML models.
Global Interpretability – Use feature importance metrics like Permutation Feature Importance (PFI) &  H-statistics.
Local Interpretability – Utilize LIME, SHAP, and partial dependence plots to explain individual predictions.

Model Wrapping

MoDeVa provides a flexible way to integrate and validate models from various environments:

Scikit-learn and Custom Models – Wrap models in a standardised interface for seamless validation. 
Arbitrary Model Wrapping – Enable validation of models outside traditional ML frameworks using custom prediction functions.
Scored Model Wrapping – Integrate vendor-provided models where direct access to internals is restricted but scored predictions are available.

Diagnostic Suite

Comprehensive Testing – Assess model reliability, fairness, and compliance through automated validation pipelines.
Automated Documentation – Generate detailed validation reports for auditability and regulatory compliance.

Model Deployment

Calibration – Calibrated probability outputs for more accurate probability prediction. 
Conformal Prediction – Generate prediction interval with statistical guarantee. 
Monitoring without Output Labels – Monitor model performance without waiting to get actual outcomes.

Features and Functionalities

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Core Capabilities

Interactive Visualisations – Generate dynamic statistical dashboards to explore data, assess models, and derive insights. 

Seamless Integrations – Supports MLflow, external MLOps pipelines, and enterprise AI workflows. 

Experiment Tracking – Keep a detailed record of datasets, model versions, and performance benchmarks. 

Multi-Mode Access – Choose between high-code (API-driven), low-code (menu-based Jupyter notebooks), and automated pipeline execution.

Special Features

User-Friendly Low-Code Panels – Simplified panels for Jupyter Notebook users, offering intuitive workflows for model validation.
Automated Pipeline Execution – Streamline complex validation tasks with chain-of-task execution and automated reporting. 
Integration with External MLOps Pipelines – Ensure compatibility with various enterprise infrastructures. 
Advanced Statistical Diagnostics – Leverage state-of-the-art statistical methods to assess model reliability and fairness.