#Experimentation
Showing 20 of 20 repositories tagged #experimentation, ranked by stars
Open Source Feature Flags, Experimentation, and Product Analytics
UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured checks (covering language, code, embedding use-cases), perform root cause analysis on failure cases and give insights on how to resolve them.
GO Feature Flag is a simple, complete and lightweight self-hosted cloud native feature flag solution 100% Open Source. 🎛️
Enterprise-grade feature flag platform that you can self-host. Get started - free.
Train to 94% on CIFAR-10 in <6.3 seconds on a single A100. Or ~95.79% in ~110 seconds (or less!)
Feature flags, experiments, and remote config management with version control
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
GenAIOps with Prompt Flow is a "GenAIOps template and guidance" to help you build LLM-infused apps using Prompt Flow. It offers a range of features including Centralized Code Hosting, Lifecycle Management, Variant and Hyperparameter Experimentation, A/B Deployment, reporting for all runs and experiments and so on.
One file. Your AI coding agent becomes a scientist. 30+ experiments while you sleep.
PipelineX: Python package to build ML pipelines for experimentation with Kedro, MLflow, and more
🧪 Simple data science experimentation & tracking with jupyter, papermill, and mlflow.
RapidFire AI: Rapid AI Customization from RAG to Fine-Tuning
Library for multi-armed bandit selection strategies, including efficient deterministic implementations of Thompson sampling and epsilon-greedy.
Transparent, robust and trustworthy A/B experimentation for Shopping feeds.
moai is a PyTorch-based AI Model Development Kit (MDK) created to improve data-driven model workflows, design and reproducibility.
Track and Collaborate on ML & AI Experiments.
Open Source version of SigOpt API, performing hyperparameter optimization and visualization
Create your own study by cloning and editing configs; or check out the code behind the study components.
A Fog Computing Emulation Framework
Long-form article introducing Decision Safety: a trust gate between dashboards and actions. Defines four pillars (coverage, freshness, stability, measurement risk), proposes a Decision Safety Score (0–100), shows common failure modes, and includes a copy-paste “Decision Safety Contract”+checklist to block unsafe decisions without hiding dashboards.