ml-infrastructure

The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.

Build, Manage and Deploy AI/ML Systems

Democratizing Reinforcement Learning for LLMs

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

Efficient Deep Learning Systems course materials

Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure.

🚀 Metadata tracking and UI service for Metaflow!

A Collection of GitHub Actions That Facilitate MLOps

Utilities for preprocessing text for deep learning with Keras

MONAI Deploy App SDK offers a framework and associated tools to design, develop and verify AI-driven applications in the healthcare imaging domain.

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

Run GPU inference and training jobs on serverless infrastructure that scales with you.

The official python package for NimbleBox. Exposes all APIs as CLIs and contains modules to make ML 🌸

Run GPU or batch jobs directly from your dev environment

A standalone inference server for trained Rubix ML estimators.

Distributed vector search / feature store in Go — high-throughput, low-latency feature serving for ML.

GPU-aware inference mesh for large-scale AI serving

Example ML projects that use the Determined library.

A tool for training models to Vertex on Google Cloud Platform.

Render Jupyter Notebooks With Metaflow Cards