awesome-production-machine-learning
A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning
GraphCanon updated Oct 4, 2026 · GitHub synced Oct 4, 2026
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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 (0d since push)
- As of Oct 4, 2026
- Provenance
- Not a fork · Organization account
- As of Oct 4, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
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)
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Similar tools
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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, Oct 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...
For agents
This page has a .md twin and JSON over the API.