---
title: "awesome-production-machine-learning vs awesome-open-mlops"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/ethicalml-awesome-production-machine-learning-vs-fuzzylabs-awesome-open-mlops"
tools: ["ethicalml-awesome-production-machine-learning", "fuzzylabs-awesome-open-mlops"]
---

# awesome-production-machine-learning vs awesome-open-mlops

*GraphCanon updated Aug 4, 2026*

## Verdict

Pick awesome-production-machine-learning when license: awesome-production-machine-learning is MIT, awesome-open-mlops is Apache-2.0; pick awesome-open-mlops when license: awesome-open-mlops is Apache-2.0, awesome-production-machine-learning is MIT.

[awesome-production-machine-learning](https://ethicalml.github.io/awesome-production-machine-learning) reports 21k GitHub stars, 2.6k forks, and 31 open issues, last pushed Aug 1, 2026. [awesome-open-mlops](https://github.com/fuzzylabs/awesome-open-mlops) has 482 stars, 54 forks, and 6 open issues, last pushed May 19, 2025. Figures are from public GitHub metadata via [awesome-production-machine-learning's repository](https://github.com/EthicalML/awesome-production-machine-learning) and [awesome-open-mlops's repository](https://github.com/fuzzylabs/awesome-open-mlops).

| | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | [awesome-open-mlops](/tools/fuzzylabs-awesome-open-mlops.md) |
| --- | --- | --- |
| Tagline | A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning | Model deployment and serving guide with open-source MLOps tools |
| Stars | 20,821 | 482 |
| Forks | 2,590 | 54 |
| Open issues | 31 | 6 |
| Language | - | - |
| Adopt for | - | awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT license making it free for use in both personal and commercial projects without requiring royalty payment or source-code disclosure. | Apache 2.0 licensed, compatible with other Apache software, promoting free use in both commercial and non-commercial contexts. |
| Categories | Data & Retrieval, Evaluation & Observability, Inference & Serving | Inference & Serving |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | [awesome-open-mlops](/tools/fuzzylabs-awesome-open-mlops.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Dormant (18%) |
| Days since push | 3d | 442d |
| Open issues (now) | 31 | 6 |
| Full report | [trust report](/tools/ethicalml-awesome-production-machine-learning/trust.md) | [trust report](/tools/fuzzylabs-awesome-open-mlops/trust.md) |

## Decision facts: awesome-production-machine-learning

- **License detail:** MIT license making it free for use in both personal and commercial projects without requiring royalty payment or source-code disclosure.

## Decision facts: awesome-open-mlops

- **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.
- **Adopt for:** awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs.
- **License detail:** Apache 2.0 licensed, compatible with other Apache software, promoting free use in both commercial and non-commercial contexts.

## Choose when

### Choose awesome-production-machine-learning if…

- License: awesome-production-machine-learning is MIT, awesome-open-mlops is Apache-2.0.
- Tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment.
- Also covers Data & Retrieval, Evaluation & Observability.
- If you need a diverse set of open-source tools for end-to-end production machine learning tasks

### Choose awesome-open-mlops if…

- License: awesome-open-mlops is Apache-2.0, awesome-production-machine-learning is MIT.
- No specific details available.
- Pricing: `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..
- Tags unique to awesome-open-mlops: datascience, devops, infrastructure, machine-learning.
- When seeking a comprehensive list of open-source models focused on deploying and serving ML models for REST API use cases

## When NOT to use awesome-production-machine-learning

- 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
- For teams preferring vendor-specific solutions over open-source options

## When NOT to use awesome-open-mlops

- 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

## Common questions

### What is the difference between awesome-production-machine-learning and awesome-open-mlops?

awesome-production-machine-learning: A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning. awesome-open-mlops: Model deployment and serving guide with open-source MLOps tools. See the comparison table for live GitHub stats and shared categories.

### When should I choose awesome-production-machine-learning over awesome-open-mlops?

Choose awesome-production-machine-learning over awesome-open-mlops when License: awesome-production-machine-learning is MIT, awesome-open-mlops is Apache-2.0; Tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment; Also covers Data & Retrieval, Evaluation & Observability; If you need a diverse set of open-source tools for end-to-end production machine learning tasks.

### When should I choose awesome-open-mlops over awesome-production-machine-learning?

Choose awesome-open-mlops over awesome-production-machine-learning when License: awesome-open-mlops is Apache-2.0, awesome-production-machine-learning is MIT; No specific details available; Pricing: `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.; Tags unique to awesome-open-mlops: datascience, devops, infrastructure, machine-learning; When seeking a comprehensive list of open-source models focused on deploying and serving ML models for REST API use cases.

### When should I avoid awesome-production-machine-learning?

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 For teams preferring vendor-specific solutions over open-source options

### When should I avoid awesome-open-mlops?

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

### Is awesome-production-machine-learning or awesome-open-mlops more popular on GitHub?

awesome-production-machine-learning has more GitHub stars (20,821 vs 482). Stars measure visibility, not whether either tool fits your constraints.

### Are awesome-production-machine-learning and awesome-open-mlops open source?

Yes - both are open-source projects on GitHub (awesome-production-machine-learning: MIT, awesome-open-mlops: Apache-2.0).

### Where can I find alternatives to awesome-production-machine-learning or awesome-open-mlops?

GraphCanon lists graph-backed alternatives at [awesome-production-machine-learning alternatives](/tools/ethicalml-awesome-production-machine-learning/alternatives) and [awesome-open-mlops alternatives](/tools/fuzzylabs-awesome-open-mlops/alternatives) ([awesome-production-machine-learning markdown twin](/tools/ethicalml-awesome-production-machine-learning/alternatives.md), [awesome-open-mlops markdown twin](/tools/fuzzylabs-awesome-open-mlops/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/ethicalml-awesome-production-machine-learning-vs-fuzzylabs-awesome-open-mlops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, awesome-production-machine-learning or awesome-open-mlops?

awesome-production-machine-learning: Very active. awesome-open-mlops: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for awesome-production-machine-learning and awesome-open-mlops?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-production-machine-learning trust report](/tools/ethicalml-awesome-production-machine-learning/trust); [awesome-open-mlops trust report](/tools/fuzzylabs-awesome-open-mlops/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=ethicalml-awesome-production-machine-learning`](/api/graphcanon/graph?tool=ethicalml-awesome-production-machine-learning)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_