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Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.

Data processing for and with foundation models! 🍎 🍋 🌽 ➡️ ➡️🍸 🍹 🍷

Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more.

Mimesis is a Python library for generating fake but realistic data in multiple languages and locales.

Synthetic data generation for tabular data

Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.

Python Toolkit for Causal and Probabilistic Reasoning

SDG is a specialized framework designed to generate high-quality structured tabular data.

Synthetic data curation for post-training and structured data extraction

Synthetic data generators for tabular and time-series data

DataDreamer: Prompt. Generate Synthetic Data. Train & Align Models.   🤖💤

A lightweight library for generating synthetic instruction tuning datasets for your data without GPT.

Design, conduct and analyze results of AI-powered surveys and experiments. Simulate social science and market research with large numbers of AI agents and LLMs.

AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.

High-fidelity synthetic financial data generator using Heston Stochastic Volatility and Jump Diffusion.

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