An awesome & curated list of best LLMOps tools for developers
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Updated
May 21, 2026 - Shell
An awesome & curated list of best LLMOps tools for developers
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
deployKF builds machine learning platforms on Kubernetes. We combine the best of Kubeflow, Airflow†, and MLflow† into a complete platform.
Docker images for fastai
🕹️ Performance Comparison of MLOps Engines, Frameworks, and Languages on Mainstream AI Models.
MONAI Deploy aims to become the de-facto standard for developing, packaging, testing, deploying and running medical AI applications in clinical production.
Run GPU inference and training jobs on serverless infrastructure that scales with you.
obra/superpowers extended — 14→24 skills, 6 GadaaLabs engineering improvements, override infrastructure, one-command install
Chart for deploying ChromaDB in Kubernetes
An Agent Skill for the DL experiment lifecycle: RUN (a GPU you own or rent) → VERIFY the number is real → DELIVER reproducible, single-source figures and tables.
Serve the home! Inference stack for your Nvidia DGX Spark aka the Grace Blackwell AI supercomputer on your desk. Mostly vLLM based for now and single-spark. For the not-so-rich buddies. If you want latest/in-testing, look at the branches
Serving Next Generation Experimental Tracking for Machine Learning Operations
Hands-on GPU/HPC infrastructure operations: K8s GPU scheduling, HAMi sharing, Slurm, observability & vLLM inference. Learn it free on a laptop; validate on one cheap GPU.
🚀 The Ultimate Curated List of LLMOps Tools, Frameworks, and Resources - A comprehensive collection of the best tools for Large Language Model Operations
mlflow container setup for docker, docker compose and kubernetes including helm chart
Easily Deploy your Tensorflow models to Heroku with just the click of a button!
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