awesome-ai-infrastructure

A platform for orchestrating complex workflows, commonly used in machine learning pipelines.

A tool for version control and reproducibility in machine learning projects.

A platform for orchestrating machine learning workflows on Kubernetes.

A human-centric framework for building and managing real-life data science projects, developed by Netflix.

An open-source platform for managing the end-to-end machine learning lifecycle.

An extensible MLOps framework for creating portable, production-ready machine learning pipelines.

An open-source automation tool for provisioning and managing infrastructure.

A service for automating AWS resource deployment and management.

An infrastructure management tool for Google Cloud Platform.

Infrastructure as code for deploying and managing cloud infrastructure using programming languages.

A tool for building, changing, and versioning infrastructure safely and efficiently.

A data management framework that simplifies incremental data processing and streaming analytics.

An open-source storage layer that brings reliability to data lakes.

An open-source feature store for managing and serving machine learning features.

A tool for data validation and testing in machine learning workflows.

An open-source data versioning platform for managing data lakes.

A deep learning compiler stack for optimizing models on various hardware backends.

A toolkit for optimizing and deploying AI inference on Intel hardware.

A high-performance deep learning inference optimizer and runtime.

An AI model optimization platform for efficient deployment on edge and cloud.

Tools for optimizing model performance through quantization.

A comprehensive platform for building, training, and deploying machine learning models on AWS.

A cloud-based platform for training, deploying, and managing machine learning models.

Google Cloud’s integrated environment for AI development and deployment.

A suite of tools for data science, machine learning, and AI model development.

A CNCF Sandbox multi-cluster Kubernetes management dashboard for deploying and observing workloads across edge and cloud infrastructure.

A cloud platform for developing, training, and deploying machine learning models.

Example projects and tutorials for using AWS SageMaker.

A course on MLOps best practices for machine learning projects.

Training resources on MLOps and model deployment.

Official documentation and guides for using Kubeflow.

A tutorial on distributed training with PyTorch.

A guide to building robust ML applications in production.

A comprehensive guide to building scalable and reliable data systems.

A book on building scalable machine learning infrastructure.

A book on creating resilient machine learning infrastructure.

A deep learning optimization library that makes distributed training easy and efficient.

A distributed deep learning training framework for TensorFlow, Keras, and PyTorch.

Using the Message Passing Interface (MPI) standard for distributed machine learning.

A framework for building scalable distributed applications, including distributed AI and reinforcement learning.