Popular GitHub Repositories for Model Selection

Visual analysis and diagnostic tools to facilitate machine learning model selection.

EvalML is an AutoML library written in python.

A comprehensive library for machine learning and numerical computing. Apply Machine Learning with Rust leveraging first principles.

Multi-Faceted AI Agent and Workflow Autotuning. Automatically optimizes LangChain, LangGraph, DSPy programs for better quality, lower execution latency, and lower execution cost. Also has a simple agent/workflow framework

Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

A detailed summary of "Designing Machine Learning Systems" by Chip Huyen. This book gives you and end-to-end view of all the steps required to build AND OPERATE ML products in production. It is a must-read for ML practitioners and Software Engineers Transitioning into ML.

Mixing Language Models with Self-Verification and Meta-Verification

Code for "Unsupervised Model Selection for Time-series Anomaly Detection", ICLR 2023.

Random Forests in Apache Spark

Lightweight and modular MLOps library targeted at small teams or individuals

[NeurIPS 2023] Official Pytorch code for LOVM: Language-Only Vision Model Selection

The code repository for "Model Spider: Learning to Rank Pre-Trained Models Efficiently"

Sequential Monte Carlo sampler for PyMC2 models.

Code release for Adversarial Branch Architecture Search for Unsupervised Domain Adaptation

Bayesian hyperparamter tuning for Spark MLLib

Search for a model and corresponding hyperparameters that best model your data

A toolkit for automatically selecting a suitable classification model along with an assessment of how hard or easy to learn a classifier on the given dataset.

[ICDE 2024] a Web-app for Evaluation of Model selection for Anomaly Detection in Time Series

Simple python package for creating predictive models

Systematic Evaluation of CASH Search Strategies for Unsupervised Anomaly Detection