Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Experiment tracking, model registries, feature stores, workflow schedulers and deployment tooling.
Signals: mlops, machine-learning-operations, experiment-tracking, model-registry, feature-store, ml-platform, kubeflow, mlflow
200 results
Complete deep learning project developed in Full Stack Deep Learning, 2022 edition. Generated automatically from https://github.com/full-stack-deep-learning/fsdl-text-recognizer-2022
Collection of reproducible deep learning for compressive sensing
Deep Learning Experiment Management
Example deep learning projects that use wandb's features.
List of Deep Learning Cloud Providers
A guideline for building practical production-level deep learning systems to be deployed in real world applications.
全语言制品仓库，涵盖npm、Maven、PyPi、Docker、Gradle、SBT、Cocoapods、Swift、RPM、Debian、PHP、Go、Pub、Ivy、NuGet、Conda、Cargo、Conan、Yarn、GitLFS、Helm、OHPM等主流工具,涵盖Huggingface 等主流AI模型仓库的代理与同步
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🤖 MLE-Agent: Your intelligent companion for seamless AI engineering and research. 🔍 Integrate with arxiv and paper with code to provide better code/research plans 🧰 OpenAI, Anthropic, Gemini, Ollama, etc supported. :fireworks: Code RAG
Run all your local AI together in one package - Ollama, Supabase, n8n, Open WebUI, and more!
nannyml: post-deployment data science in python
☁️ Build multimodal AI applications with cloud-native stack
Turn expensive prompts into cheap fine-tuned models
Learn to build your Second Brain AI assistant with LLMs, agents, RAG, fine-tuning, LLMOps and AI systems techniques.
🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴
The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices
Serverless Machine Learning Course for building AI-enabled Prediction Services from models and features
Hopsworks - Data-Intensive AI platform with a Feature Store
All the available resources to master MLOPS from scratch
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🌀 𝗧𝗵𝗲 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝟳-𝗦𝘁𝗲𝗽𝘀 𝗠𝗟𝗢𝗽𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 | 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟𝗘 & 𝗠𝗟𝗢𝗽𝘀 for free by designing, building and deploying an end-to-end ML batch system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 2.5 𝘩𝘰𝘶𝘳𝘴 𝘰𝘧 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 & 𝘷𝘪𝘥𝘦𝘰 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴
🤖Self-Modifying Framework from the Future 🔮 World's First AMS
A framework for comprehensive diagnosis and optimization of agents using simulated, realistic synthetic interactions
A work in progress to build out solutions in Rust for MLOPs
24,545 repositories in the index in total.
