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.

Decentralized GPU compute marketplace on Bittensor Subnet 51 for AI and machine learning workloads.

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.

Self-hosted OpenAI-compatible gateway for multi-provider LLM routing, fallbacks, load balancing, and observability.

A Kubernetes-based model serving solution as part of the Kubeflow project.

A scalable model serving platform supporting multiple frameworks.

A cross-platform, high-performance scoring engine for serving ONNX models.

An open-source platform for deploying and monitoring machine learning models on Kubernetes.

A flexible, high-performance serving system for machine learning models.

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.