Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

Validot is a performance-first, compact library for advanced model validation. Using a simple declarative fluent interface, it efficiently handles classes, structs, nested members, collections, nullables, plus any relation or combination of them. It also supports translations, custom logic extensions with tests, and DI containers.

scores: Metrics for the verification, evaluation and optimisation of forecasts, predictions or models.

📚 A curated list of papers & technical articles on AI Quality & Safety

Tools for validating OpenAPI (Swagger) files.

MTSS-GAN: Multivariate Time Series Simulation with Generative Adversarial Networks (by @firmai)

Model verification, validation, and error analysis

Overcoming limitation of Rails model errors API

Forecast Verification/Validation Tools in Python

Laravel model validation validates the data on model events. *Only for laravel applications.

Code repository for the manuscript: 'Assessing performance in prediction models with survival outcomes: practical guidance for Cox proportional hazards models' (published in Annals of Internal Medicine)

Code repository for the manuscript 'Validation of the performance of competing risks prediction models: a guide through modern methods' (2023, BMJ)

🌻A modular DI, MVC, and Model binding/validation framework for NodeJS and TypeScript or ES6

Machine learning tools for the analysis of systems serology data

Labs for the SP21/22 Machine Learning class - Prof. Cesare Alippi, USI INF-BSc

CogniBench is a software framework for benchmarking cognitive models

An R package with useful functions to diagnose INLA models

Model recovery analysis for intertemporal choice

The Disease Prediction Project uses AI/ML to predict diseases based on selected symptoms, designed for low-resource communities. It delivers fast, accurate predictions using a MLP model, offering tailored, efficient diagnostics for areas with limited healthcare access.

Model versioning using Weight&Biases with Python.