GCP Cloud Functions support for ASGI applications

Statsmodels: statistical modeling and econometrics in Python

The official Python library for the Google Gemini API

OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

Autonomous coding agent right in your IDE, capable of creating/editing files, executing commands, using the browser, and more with your permission every step of the way.

Structured Text Generation

The fastest way to create an HTML app

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.

A JAX research toolkit for building, editing, and visualizing neural networks.

Make awesome display tables using Python.

A simple, performant and scalable Jax LLM!

Synthetic data generation for tabular data

A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.

Easily use and train state of the art late-interaction retrieval methods (ColBERT) in any RAG pipeline. Designed for modularity and ease-of-use, backed by research.

This repository compiles code samples and notebooks demonstrating how to use Generative AI on Google Cloud Vertex AI.

Build MLOps Pipelines in Minutes

Google Cloud Client Library for Python

Monte Carlo tree search in JAX

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

Causal Inference and Discovery in Python by Packt Publishing

Integrate cutting-edge LLM technology quickly and easily into your apps

A Python SDK for Vertex AI, a fully managed, end-to-end platform for data science and machine learning.

Streamdeck plugin stock ticker to watch your assets (market and crypto)

BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.

Sample code and notebooks for Generative AI on Google Cloud, with Gemini on Vertex AI

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

R package for interacting with Google Cloud Vertex AI API