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Machine Learning Operations - An awesome list of references for MLOps.

Open source MLOps project that eases model handoffs between data scientist and DevOps.

Open source MLOps platform that helps you collaborate, reproduce and share your ML work.

MLOps framework to package, deploy, monitor and manage thousands of production machine learning models.

An MLOps/LLMOps platform for model building, evaluation, and fine-tuning.

A machine learning / bayesian inference assigning attributes to objects.

Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.

Open-source project that provides machine-learning models, research, training resources, or evaluation tools.

Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.

Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.

Drop-in AsyncOpenAI replacement that transparently batches requests via the Batch API for cheaper LLM inference.

Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis).

Bayesian Inference Tools in Python.

Open-source platform for high-performance ML model serving.

An open-source LLM gateway with routing, load balancing, guardrails, and observability for 1000+ models. opensource.

Official inference framework for 1-bit LLMs, by Microsoft. opensource.

Deploy a ML inference service on a budget in less than 10 lines of code.

Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.

An easy-to-use feature store. Optimized for time-series data.

Open-source project that supports AI agents, automated workflows, orchestration, or delegated tasks.

Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.

Open-source tool that lets you package ML models in a standard, production-ready container.

Open-source project that provides machine-learning models, research, training resources, or evaluation tools.

Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.