Awesome AI Infrastructure

     

A curated list of awesome tools, frameworks, platforms, and resources for building scalable and efficient AI infrastructure, including distributed training, model serving, MLOps, and deployment.

Contents

• Distributed Training
• Model Serving and Deployment
• MLOps and Automation
• Data Management
• Optimization Tools
• Infrastructure as Code
• Cloud Platforms
• Learning Resources
• Books
• Community
• Contribute
• License

Distributed Training

• Horovod - A distributed deep learning training framework for TensorFlow, Keras, and PyTorch.
• Ray - A framework for building scalable distributed applications, including distributed AI and reinforcement learning.
• PyTorch Distributed - Tools and libraries for distributed training in PyTorch.
• DeepSpeed - A deep learning optimization library that makes distributed training easy and efficient.
• MPI for Machine Learning - Using the Message Passing Interface (MPI) standard for distributed machine learning.

Model Serving and Deployment

• TensorFlow Serving - A flexible, high-performance serving system for machine learning models.
• TorchServe - A model serving framework for PyTorch, providing fast and efficient model deployment.
• NVIDIA Triton Inference Server - A scalable model serving platform supporting multiple frameworks.
• ONNX Runtime - A cross-platform, high-performance scoring engine for serving ONNX models.
• Seldon Core - An open-source platform for deploying and monitoring machine learning models on Kubernetes.
• KFServing (KServe) - A Kubernetes-based model serving solution as part of the Kubeflow project.

MLOps and Automation

• MLflow - An open-source platform for managing the end-to-end machine learning lifecycle.
• Kubeflow - A platform for orchestrating machine learning workflows on Kubernetes.
• DVC (Data Version Control) - A tool for version control and reproducibility in machine learning projects.
• ZenML - An extensible MLOps framework for creating portable, production-ready machine learning pipelines.
• Airflow - A platform for orchestrating complex workflows, commonly used in machine learning pipelines.
• Metaflow - A human-centric framework for building and managing real-life data science projects, developed by Netflix.

Data Management

• Delta Lake - An open-source storage layer that brings reliability to data lakes.
• Apache Hudi - A data management framework that simplifies incremental data processing and streaming analytics.
• Feast - An open-source feature store for managing and serving machine learning features.
• Great Expectations - A tool for data validation and testing in machine learning workflows.
• LakeFS - An open-source data versioning platform for managing data lakes.

Optimization Tools

• NVIDIA TensorRT - A high-performance deep learning inference optimizer and runtime.
• Apache TVM - A deep learning compiler stack for optimizing models on various hardware backends.
• Intel OpenVINO - A toolkit for optimizing and deploying AI inference on Intel hardware.
• OctoML - An AI model optimization platform for efficient deployment on edge and cloud.
• Quantization Aware Training (QAT) - Tools for optimizing model performance through quantization.

Infrastructure as Code

• Terraform - A tool for building, changing, and versioning infrastructure safely and efficiently.
• Pulumi - Infrastructure as code for deploying and managing cloud infrastructure using programming languages.
• Ansible - An open-source automation tool for provisioning and managing infrastructure.
• AWS CloudFormation - A service for automating AWS resource deployment and management.
• Google Deployment Manager - An infrastructure management tool for Google Cloud Platform.

Cloud Platforms

• AWS SageMaker - A comprehensive platform for building, training, and deploying machine learning models on AWS.
• Google AI Platform - Google Cloud’s integrated environment for AI development and deployment.
• Azure Machine Learning - A cloud-based platform for training, deploying, and managing machine learning models.
• IBM Watson Studio - A suite of tools for data science, machine learning, and AI model development.
• Paperspace Gradient - A cloud platform for developing, training, and deploying machine learning models.

Learning Resources

• Coursera: MLOps Fundamentals - A course on MLOps best practices for machine learning projects.
• Google Cloud: ML Operations - Training resources on MLOps and model deployment.
• AWS SageMaker Workshops - Example projects and tutorials for using AWS SageMaker.
• Kubeflow Documentation - Official documentation and guides for using Kubeflow.
• PyTorch Distributed Training Guide - A tutorial on distributed training with PyTorch.

Books

• Machine Learning Engineering by Andriy Burkov - A book on building scalable machine learning infrastructure.
• Building Machine Learning Powered Applications by Emmanuel Ameisen - A guide to building robust ML applications in production.
• Designing Data-Intensive Applications by Martin Kleppmann - A comprehensive guide to building scalable and reliable data systems.
• MLOps: Data Science in Production by Mark Treveil and The Dotscience Team - A book on best practices for MLOps and model deployment.
• Reliable Machine Learning by Cathy Chen - A book on creating resilient machine learning infrastructure.

Community

• MLOps Community - A global community focused on MLOps and AI infrastructure.
• Reddit: r/MachineLearning - A subreddit for discussions on machine learning infrastructure and tools.
• Kubeflow Slack - A Slack community for discussing Kubeflow and machine learning pipelines.
• Paperspace Forums - A community forum for discussing machine learning infrastructure and tools.
• GitHub: MLOps Repositories - A collection of open-source MLOps projects on GitHub.

Contribute

Contributions are welcome!

License