Learn how to develop, deploy and iterate on production-grade ML applications.

Superduper: End-to-end framework for building custom AI applications and agents.

Learn how to design, develop, deploy and iterate on production-grade ML applications.

deploy ML Infrastructure and MLOps tooling anywhere quickly and with best practices with a single command

Nerlnet is a framework for research and development of distributed machine learning models on IoT

Dynamic resources changes for multi-dimensional parallelism training

A fully adaptive, zero-tuning parameter manager that enables efficient distributed machine learning training

🍎👉🍏 Everything you need in order to get started building distributed machine learning with Apple's MLX

Repository that contains the code for the paper titled, 'Unifying Distillation with Personalization in Federated Learning'.

Elixir SDK for the Tinker ML platform—LoRA training, sampling, and service orchestration built on OTP, Finch, and telemetry.

A visual ebook on distributed ML with PCCL, built for students for easy read (educational only) (shoutout to owners: https://github.com/PrimeIntellect-ai/pccl)

🌍 Generate realistic, dynamic environments for robotics research using Isaac Sim. Manage simulations, control robots, and customize workflows easily.

Rust-based distributed ML inference with zero-knowledge proofs for privacy-preserving and verifiable computation.

Network-Aware Federated Optimisation (NAFO): a 5G-native framework that jointly optimizes wireless channel conditions, differential privacy, and multi-modal healthcare AI. Demonstrates robust federated learning under realistic 3GPP network constraints with AoI-aware dynamics.

Caffe: a fast open framework for deep learning. Caffe-pslite: run deep learning in a cluster with ps-lite (including SSP model)

A simulation framework for federated learning experiments, allowing researchers to test and evaluate privacy-preserving machine learning algorithms on decentralized datasets.

Federated learning utilities for distributed threat detection — Flower integration with differential privacy and secure aggregation

This project demonstrates how to build a recommendation system using Apache Spark for distributed data processing, Python for machine learning, and common data science libraries. It uses the Alternating Least Squares (ALS) algorithm for collaborative filtering.

Rust fork of RL4Sys; Early Single-Agent Distributed Reinforcement Learning Framework written in Rust & exported to Python

Federated phishing detection platform combining privacy-preserving learning and distributed cybersecurity analytics.