Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Topic Modelling for Humans
Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automating these high-value generic R&D processes through R&D-Agent, which lets AI drive data-driven AI. 🔗https://aka.ms/RD-Agent-Tech-Report
Low-code framework for building custom LLMs, neural networks, and other AI models
Statsmodels: statistical modeling and econometrics in Python
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Lightning ⚡️ fast forecasting with statistical and econometric models.
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
moDel Agnostic Language for Exploration and eXplanation
[CONTRIBUTORS WELCOME] Generalized Additive Models in Python
A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python
Build, test, deploy, iterate - Dev and prod tool for data science pipelines
Python implementation of the goldeneye algorithm to investigate how classifiers utilise the structure of a dataset.