Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

Diffusers-Interpret 🤗🧨🕵️‍♀️: Model explainability for 🤗 Diffusers. Get explanations for your generated images.

A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀

A python library to send data to Arize AI!

code for studying OpenAI's CLIP explainability

Sample notebooks that use the Openlayer Python API

CrysXPP: An Explainable Property Predictor for Crystalline Materials (NPJ Computational Materials - 2022)

Java client to interact with Arize API

Explaining Trees (LightGBM) with FastTreeShap (Shapley) and What if tool

Capture fundamentals around ethics of AI, responsible AI from principle, process, standards, guidelines, ecosystem, regulation/risk standpoint.

Example projects for Arthur Model Monitoring Platform

The official Python library for Openlayer, the Continuous Model Improvement Platform for AI. 📈

A proof-of-concept for the implementation of an early fault detection system in oil wells, designed to enhance operational efficiency and reduce costs.

A reusable codebase for fast data science and machine learning experimentation, integrating various open-source tools to support automatic EDA, ML models experimentation and tracking, model inference, model explainability, bias, and data drift analysis.

This project is a machine learning competition hosted on Kaggle platform, focused on forecasting Walmart's monthly and quarterly sales. We tasked with developing advanced predictive models to accurately predict Walmart's sales, taking into account various factors such as historical sales data, macroeconomic indicators, and local market conditions.

Machine Learning Individual Project - November 23, 2021

Developed an efficient system to empower retailers with profitable insights & maintain a competitive edge in the dynamic retail industry.

Study on the performance of pre-trained models (VGG16, EfficientNetb0, ResNet50, ViT16) with weight fine tuning, as well as classical ML algorithms (Naive Bayes, Logistic Regression, Random Forest) on a dataset of 6.806 fungi microscopy Images utilizing Pytorch.

The Fraud Detection project aims to improve identification of fraudulent activities in e-commerce and banking by developing advanced machine learning models that analyze transaction data, employ feature engineering, and implement real-time monitoring for high accuracy fraud detection.

An application of the WhizML codebase for an analysis of cardiovascular disease risk.