ml-infrastructure

Build, Manage and Deploy AI/ML Systems

Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage all AI compute (Kubernetes, 17+ clouds, or on-prem).

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 (HSE, YSDA)

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

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

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

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 🌸

A standalone inference server for trained Rubix ML estimators.

Run GPU or batch jobs directly from your dev environment

cluster/scheduler health monitoring for GPU jobs on k8s

Example ML projects that use the Determined library.

Kubeflow blog based on fastpages

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

Render Jupyter Notebooks With Metaflow Cards

GPU-aware inference mesh for large-scale AI serving