ghbacct

maestro

Maestro: Netflix’s Workflow Orchestrator

datachain

DataChain 🔗 AI-dataframe to enrich, transform and analyze data from cloud storages for ML training and LLM apps

pdfly

CLI tool to extract (meta)data from PDF and manipulate PDF files

BERTopic

Leveraging BERT and c-TF-IDF to create easily interpretable topics.

Top2Vec

Top2Vec learns jointly embedded topic, document and word vectors.

texthero

Text preprocessing, representation and visualization from zero to hero.

token-list

prodigy-tui

A textual TUI for Prodigy

machine-learning-for-software-engineers

A complete daily plan for studying to become a machine learning engineer.

100-Days-Of-ML-Code

100 Days of ML Coding

data-science-interviews

Data science interview questions and answers

gpt-investor

dockerLLM

TheBloke's Dockerfiles

aici

AICI: Prompts as (Wasm) Programs

Daft

Distributed DataFrame for Python designed for the cloud, powered by Rust

drawdata

Draw datasets from within Jupyter.

feature-engineering-az

Source for book "Feature Engineering A-Z"

hamilton

Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.

llm-course

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

mergekit

Tools for merging pretrained large language models.

sglang

SGLang is yet another fast serving framework for large language models and vision language models.

Flowise

Drag & drop UI to build your customized LLM flow

self-rag

This includes the original implementation of SELF-RAG: Learning to Retrieve, Generate and Critique through self-reflection by Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi.

datatrove

Freeing data processing from scripting madness by providing a set of platform-agnostic customizable pipeline processing blocks.

instructor

structured outputs for llms

shaderunner

Ctrl + F but fancy.

newsy

ML-Papers-Explained

Explanation to key concepts in ML

projects

🪐 End-to-end NLP workflows from prototype to production

nlpaug

Data augmentation for NLP