awesome-open-mlops
Model deployment and serving guide with open-source MLOps tools
GraphCanon updated Oct 4, 2026 · GitHub synced Oct 4, 2026
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Decision brief
awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs.
Good fit when
- When seeking a comprehensive list of open-source models focused on deploying and serving ML models for REST API use cases
- If your project requires Apache 2.0 licensed tools to ensure compatibility with other Apache-licensed software components
Avoid when
- Avoid if you need proprietary or commercial MLOps solutions that offer enterprise support or features not covered by open-source projects
- Not suitable for scenarios where model serving frameworks outside of the curated list, such as those under different licenses like AGPL-3.0 used by BodyworkML, are required
- Hosting:
- unknown - No specific details available.
- Pricing:
- freemium - `awesome-open-mlops` is freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (502d 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/fuzzylabs/awesome-open-mlopsHow 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
A curated list of open-source MLOps projects focused on model deployment and serving.
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
Categories
Tags
README
Model deployment and serving Model serving is the process of taking a trained model and presenting it behind a REST API, and this enables other software components to interact with a model. To make deployment of these model servers as simple as possible, it's commonplace to run t...
For agents
This page has a .md twin and JSON over the API.