awesome-production-machine-learning
A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning
GraphCanon updated 2w · GitHub synced 2w · 26 views this month
Decision brief
Good fit when
- If you need a diverse set of open-source tools for end-to-end production machine learning tasks
- For organizations looking to expand their toolbox with various frameworks, from deployment to observability
Avoid when
- If you seek a comprehensive solution integrated into one platform rather than selecting from diverse tools
- When your project is specific to only one aspect of machine learning like just deployment or monitoring, and not for the entire workflow
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (3d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/EthicalML/awesome-production-machine-learningHow it fits your stack(1)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
EthicalML/awesome-production-machine-learning is an aggregation of tools to support the deployment, observation, version control, and scalability needs of production-level machine learning systems. It includes a varied selection from frameworks focused on model serving capabilities to observability solutions for AI features.
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 4, 2026)
late](https://github.com/facebookincubator/AITemplate) - AITemplate (AIT) is a Python framework that transforms deep neural networks into CUDA (NVIDIA GPU) / HIP (AMDSource link
Tags
README
Deployment and Serving
- Agenta - Agenta provides end-to-end tools for the entire LLMOps workflow: building (LLM playground, evaluation), deploying (prompt and configuration management), and (LLM observability and tracing).
- AirLLM - AirLLM optimizes inference memory usage, allowing 70B large language models to run inference on a single 4GB GPU card without quantization, distillation and pruning.
- AITemplate - AITemplate (AIT) is a Python framework that transforms deep neural networks into CUDA (NVIDIA GPU) / HIP (AMD GPU) C++ code for lightning-fast inference serving.
- BentoML - BentoML is an open source framework for high performance ML model serving.
- BISHENG - BISHENG is an open LLM application devops platform, focusing on enterprise scenarios.
- DeepDetect - Machine Learning production server for TensorFlow, XGBoost and Cafe models written in C++ and maintained by Jolibrain.
- Dynamo - NVIDIA Dynamo is a high-throughput, low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments.
- exo - exo helps you run your AI cluster at home with everyday devices.
- Genkit - Genkit is an open source framework for building AI-powered apps with familiar code-centric patterns. Genkit makes it easy to develop, integrate, and test AI features with observability and evaluations.
- GoModel - GoModel is a self-hosted AI gateway written in Go that exposes a unified OpenAI-compatible API across OpenAI, Anthropic, Gemini, Groq, xAI, Ollama and other providers, with routing, usage tracking, rate limits, and guardrails.
- Inference - A fast, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models. With Inference, you can deploy models such as YOLOv5, YOLOv8, CLIP, SAM, and CogVLM on your own hardware using Docker.
- Infinity - Infinity is a high-throughput, low-latency REST API for serving text-embeddings, reranking models and clip.
- IPEX-LLM - IPEX-LLM is a PyTorch library for running LLM on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max) with very low latency.
- LiteLLM - LiteLLM is a Python SDK, Proxy Server (LLM Gateway) to call 100+ LLM APIs in OpenAI format - Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, Replicate, Groq.
- LiteRT - LiteRT (formerly TensorFlow Lite) is Google's high-performance runtime for on-device AI inference, enabling deployment of machine learning models on mobile, embedded, and edge devices.
- LiteRT-LM - LiteRT-LM is Google's production-ready, high-performance inference framework for deploying Large Language Models on edge devices, with cross-platform support for Android, iOS, Web, Desktop, and IoT.
- LitServe - LitServe is a flexible serving engine for AI models built on FastAPI. It supports custom inference engines for models, agents, multi-modal systems, RAG, and complex ML pipelines.
- Jina-serve - Jina-serve is a framework for building and deploying AI services that communicate via gRPC, HTTP and WebSockets.
- Kiln - Kiln is an OSS tool for fine-tuning LLM models, synthetic data generation, and collaborating on dataset
For agents
This page has a .md twin and JSON over the API.